id sid tid token lemma pos cana-3280 1 1 communications communication NOUN cana-3280 1 2 on on ADP cana-3280 1 3 applied apply VERB cana-3280 1 4 nonlinear nonlinear ADJ cana-3280 1 5 analysis analysis NOUN cana-3280 1 6 issn issn NOUN cana-3280 1 7 : : PUNCT cana-3280 1 8 1074 1074 NUM cana-3280 1 9 - - PUNCT cana-3280 1 10 133x 133x NUM cana-3280 1 11 vol vol NOUN cana-3280 1 12 32 32 NUM cana-3280 1 13 no no NOUN cana-3280 1 14 . . PUNCT cana-3280 2 1 6s 6s NUM cana-3280 2 2 ( ( PUNCT cana-3280 2 3 2025 2025 NUM cana-3280 2 4 ) ) PUNCT cana-3280 2 5 123 123 NUM cana-3280 2 6 https://internationalpubls.com https://internationalpubls.com X cana-3280 2 7 deep deep ADJ cana-3280 2 8 custom custom NOUN cana-3280 2 9 transfer transfer NOUN cana-3280 2 10 learning learning NOUN cana-3280 2 11 models model NOUN cana-3280 2 12 for for ADP cana-3280 2 13 recognizing recognize VERB cana-3280 2 14 human human ADJ cana-3280 2 15 activities activity NOUN cana-3280 2 16 via via ADP cana-3280 2 17 video video NOUN cana-3280 2 18 surveillance surveillance NOUN cana-3280 2 19 saurabh saurabh PROPN cana-3280 2 20 gupta1 gupta1 PROPN cana-3280 2 21 , , PUNCT cana-3280 2 22 dr dr PROPN cana-3280 2 23 . . PROPN cana-3280 2 24 rajendra rajendra PROPN cana-3280 2 25 prasad prasad PROPN cana-3280 2 26 mahapatra2 mahapatra2 PROPN cana-3280 2 27 1,2 1,2 NUM cana-3280 2 28 department department NOUN cana-3280 2 29 of of ADP cana-3280 2 30 computer computer NOUN cana-3280 2 31 science science NOUN cana-3280 2 32 and and CCONJ cana-3280 2 33 engineering engineering NOUN cana-3280 2 34 , , PUNCT cana-3280 2 35 srm srm PROPN cana-3280 2 36 institute institute PROPN cana-3280 2 37 of of ADP cana-3280 2 38 science science NOUN cana-3280 2 39 and and CCONJ cana-3280 2 40 technology technology NOUN cana-3280 2 41 , , PUNCT cana-3280 2 42 delhi delhi PROPN cana-3280 2 43 ncr ncr PROPN cana-3280 2 44 campus campus PROPN cana-3280 2 45 , , PUNCT cana-3280 2 46 ghaziabad ghaziabad PROPN cana-3280 2 47 , , PUNCT cana-3280 2 48 uttar uttar PROPN cana-3280 2 49 pradesh pradesh PROPN cana-3280 2 50 saurabhg1@srmist.edu.in1 saurabhg1@srmist.edu.in1 PROPN cana-3280 2 51 , , PUNCT cana-3280 2 52 rajendrm1@srmist.edu.in2 rajendrm1@srmist.edu.in2 NOUN cana-3280 2 53 article article NOUN cana-3280 2 54 history history NOUN cana-3280 2 55 : : PUNCT cana-3280 2 56 received receive VERB cana-3280 2 57 : : PUNCT cana-3280 2 58 16 16 NUM cana-3280 2 59 - - SYM cana-3280 2 60 10 10 NUM cana-3280 2 61 - - PUNCT cana-3280 2 62 2024 2024 NUM cana-3280 2 63 revised revise VERB cana-3280 2 64 : : PUNCT cana-3280 2 65 30 30 NUM cana-3280 2 66 - - SYM cana-3280 2 67 11 11 NUM cana-3280 2 68 - - PUNCT cana-3280 2 69 2024 2024 NUM cana-3280 2 70 accepted accept VERB cana-3280 2 71 : : PUNCT cana-3280 2 72 10 10 NUM cana-3280 2 73 - - SYM cana-3280 2 74 12 12 NUM cana-3280 2 75 - - PUNCT cana-3280 2 76 2024 2024 NUM cana-3280 2 77 abstract abstract NOUN cana-3280 2 78 : : PUNCT cana-3280 2 79 the the DET cana-3280 2 80 use use NOUN cana-3280 2 81 of of ADP cana-3280 2 82 video video NOUN cana-3280 2 83 surveillance surveillance NOUN cana-3280 2 84 for for ADP cana-3280 2 85 human human ADJ cana-3280 2 86 activity activity NOUN cana-3280 2 87 recognition recognition NOUN cana-3280 2 88 ( ( PUNCT cana-3280 2 89 har har NOUN cana-3280 2 90 ) ) PUNCT cana-3280 2 91 in in ADP cana-3280 2 92 inpatient inpatient PROPN cana-3280 2 93 rehabilitation rehabilitation NOUN cana-3280 2 94 , , PUNCT cana-3280 2 95 activity activity NOUN cana-3280 2 96 recognition recognition NOUN cana-3280 2 97 , , PUNCT cana-3280 2 98 or or CCONJ cana-3280 2 99 mobile mobile ADJ cana-3280 2 100 health health NOUN cana-3280 2 101 monitoring monitoring NOUN cana-3280 2 102 has have AUX cana-3280 2 103 grown grow VERB cana-3280 2 104 in in ADP cana-3280 2 105 popularity popularity NOUN cana-3280 2 106 recently recently ADV cana-3280 2 107 . . PUNCT cana-3280 3 1 before before ADP cana-3280 3 2 using use VERB cana-3280 3 3 it it PRON cana-3280 3 4 on on ADP cana-3280 3 5 new new ADJ cana-3280 3 6 users user NOUN cana-3280 3 7 , , PUNCT cana-3280 3 8 a a DET cana-3280 3 9 har har NOUN cana-3280 3 10 classifier classifier NOUN cana-3280 3 11 is be AUX cana-3280 3 12 often often ADV cana-3280 3 13 trained train VERB cana-3280 3 14 offline offline ADJ cana-3280 3 15 with with ADP cana-3280 3 16 known know VERB cana-3280 3 17 users user NOUN cana-3280 3 18 . . PUNCT cana-3280 4 1 if if SCONJ cana-3280 4 2 the the DET cana-3280 4 3 activity activity NOUN cana-3280 4 4 patterns pattern NOUN cana-3280 4 5 of of ADP cana-3280 4 6 new new ADJ cana-3280 4 7 users user NOUN cana-3280 4 8 differ differ VERB cana-3280 4 9 from from ADP cana-3280 4 10 those those PRON cana-3280 4 11 in in ADP cana-3280 4 12 the the DET cana-3280 4 13 training training NOUN cana-3280 4 14 data datum NOUN cana-3280 4 15 , , PUNCT cana-3280 4 16 the the DET cana-3280 4 17 accuracy accuracy NOUN cana-3280 4 18 of of ADP cana-3280 4 19 this this DET cana-3280 4 20 method method NOUN cana-3280 4 21 for for ADP cana-3280 4 22 them they PRON cana-3280 4 23 can can AUX cana-3280 4 24 be be AUX cana-3280 4 25 subpar subpar ADJ cana-3280 4 26 . . PUNCT cana-3280 5 1 because because SCONJ cana-3280 5 2 of of ADP cana-3280 5 3 the the DET cana-3280 5 4 high high ADJ cana-3280 5 5 cost cost NOUN cana-3280 5 6 of of ADP cana-3280 5 7 computing computing NOUN cana-3280 5 8 and and CCONJ cana-3280 5 9 the the DET cana-3280 5 10 lengthy lengthy ADJ cana-3280 5 11 training training NOUN cana-3280 5 12 period period NOUN cana-3280 5 13 for for ADP cana-3280 5 14 new new ADJ cana-3280 5 15 users user NOUN cana-3280 5 16 , , PUNCT cana-3280 5 17 it it PRON cana-3280 5 18 is be AUX cana-3280 5 19 impractical impractical ADJ cana-3280 5 20 to to PART cana-3280 5 21 start start VERB cana-3280 5 22 from from ADP cana-3280 5 23 scratch scratch NOUN cana-3280 5 24 when when SCONJ cana-3280 5 25 building build VERB cana-3280 5 26 mobile mobile ADJ cana-3280 5 27 applications application NOUN cana-3280 5 28 . . PUNCT cana-3280 6 1 the the DET cana-3280 6 2 2dcnnlstm 2dcnnlstm NUM cana-3280 6 3 , , PUNCT cana-3280 6 4 transfer transfer VERB cana-3280 6 5 2dcnnlstm 2dcnnlstm NUM cana-3280 6 6 , , PUNCT cana-3280 6 7 lrcn lrcn PROPN cana-3280 6 8 , , PUNCT cana-3280 6 9 or or CCONJ cana-3280 6 10 transfer transfer NOUN cana-3280 6 11 lrcn lrcn PROPN cana-3280 6 12 were be AUX cana-3280 6 13 proposed propose VERB cana-3280 6 14 in in ADP cana-3280 6 15 this this DET cana-3280 6 16 paper paper NOUN cana-3280 6 17 as as ADP cana-3280 6 18 deep deep ADJ cana-3280 6 19 learning learning NOUN cana-3280 6 20 and and CCONJ cana-3280 6 21 transfer transfer VERB cana-3280 6 22 learning learning NOUN cana-3280 6 23 models model NOUN cana-3280 6 24 for for ADP cana-3280 6 25 recognizing recognize VERB cana-3280 6 26 human human ADJ cana-3280 6 27 activities activity NOUN cana-3280 6 28 via via ADP cana-3280 6 29 video video NOUN cana-3280 6 30 surveillance surveillance NOUN cana-3280 6 31 . . PUNCT cana-3280 7 1 the the DET cana-3280 7 2 transfer transfer NOUN cana-3280 7 3 lrcn lrcn PROPN cana-3280 7 4 scored score VERB cana-3280 7 5 100 100 NUM cana-3280 7 6 for for ADP cana-3280 7 7 training training NOUN cana-3280 7 8 accuracy accuracy NOUN cana-3280 7 9 and and CCONJ cana-3280 7 10 69.39 69.39 NUM cana-3280 7 11 for for ADP cana-3280 7 12 validation validation NOUN cana-3280 7 13 accuracy accuracy NOUN cana-3280 7 14 , , PUNCT cana-3280 7 15 respectively respectively ADV cana-3280 7 16 . . PUNCT cana-3280 8 1 the the DET cana-3280 8 2 lowest low ADJ cana-3280 8 3 validation validation NOUN cana-3280 8 4 loss loss NOUN cana-3280 8 5 of of ADP cana-3280 8 6 0.16 0.16 NUM cana-3280 8 7 and and CCONJ cana-3280 8 8 the the DET cana-3280 8 9 lowest low ADJ cana-3280 8 10 training training NOUN cana-3280 8 11 loss loss NOUN cana-3280 8 12 of of ADP cana-3280 8 13 0.001 0.001 NUM cana-3280 8 14 was be AUX cana-3280 8 15 obtained obtain VERB cana-3280 8 16 by by ADP cana-3280 8 17 transfer transfer NOUN cana-3280 8 18 lrcn lrcn PROPN cana-3280 8 19 , , PUNCT cana-3280 8 20 respectively respectively ADV cana-3280 8 21 . . PUNCT cana-3280 9 1 the the DET cana-3280 9 2 2dcnnlstm 2dcnnlstm PROPN cana-3280 9 3 has have VERB cana-3280 9 4 a a DET cana-3280 9 5 98.34 98.34 NUM cana-3280 9 6 lowest low ADJ cana-3280 9 7 training training NOUN cana-3280 9 8 accuracy accuracy NOUN cana-3280 9 9 and and CCONJ cana-3280 9 10 a a DET cana-3280 9 11 47.62 47.62 NUM cana-3280 9 12 lowest low ADJ cana-3280 9 13 validation validation NOUN cana-3280 9 14 accuracy accuracy NOUN cana-3280 9 15 . . PUNCT cana-3280 10 1 keywords keyword VERB cana-3280 10 2 deep deep ADJ cana-3280 10 3 learning learning NOUN cana-3280 10 4 , , PUNCT cana-3280 10 5 transfer transfer NOUN cana-3280 10 6 learning learning NOUN cana-3280 10 7 , , PUNCT cana-3280 10 8 video video NOUN cana-3280 10 9 surveillance surveillance NOUN cana-3280 10 10 , , PUNCT cana-3280 10 11 and and CCONJ cana-3280 10 12 human human ADJ cana-3280 10 13 activity activity NOUN cana-3280 10 14 recognition recognition NOUN cana-3280 10 15 1 1 NUM cana-3280 10 16 . . PUNCT cana-3280 10 17 introduction introduction NOUN cana-3280 10 18 because because SCONJ cana-3280 10 19 there there PRON cana-3280 10 20 are be VERB cana-3280 10 21 so so ADV cana-3280 10 22 many many ADJ cana-3280 10 23 possible possible ADJ cana-3280 10 24 uses use NOUN cana-3280 10 25 for for ADP cana-3280 10 26 human human ADJ cana-3280 10 27 action action NOUN cana-3280 10 28 recognition recognition NOUN cana-3280 10 29 , , PUNCT cana-3280 10 30 academics academic NOUN cana-3280 10 31 have have AUX cana-3280 10 32 devoted devote VERB cana-3280 10 33 a a DET cana-3280 10 34 lot lot NOUN cana-3280 10 35 of of ADP cana-3280 10 36 attention attention NOUN cana-3280 10 37 to to ADP cana-3280 10 38 studying study VERB cana-3280 10 39 it it PRON cana-3280 10 40 during during ADP cana-3280 10 41 the the DET cana-3280 10 42 past past ADJ cana-3280 10 43 ten ten NUM cana-3280 10 44 years year NOUN cana-3280 10 45 . . PUNCT cana-3280 11 1 these these PRON cana-3280 11 2 include include VERB cana-3280 11 3 autonomous autonomous ADJ cana-3280 11 4 driving driving NOUN cana-3280 11 5 , , PUNCT cana-3280 11 6 surveillance surveillance NOUN cana-3280 11 7 , , PUNCT cana-3280 11 8 ambient ambient NOUN cana-3280 11 9 - - PUNCT cana-3280 11 10 supported support VERB cana-3280 11 11 living living NOUN cana-3280 11 12 , , PUNCT cana-3280 11 13 entertainment entertainment NOUN cana-3280 11 14 , , PUNCT cana-3280 11 15 & & CCONJ cana-3280 11 16 human human ADJ cana-3280 11 17 - - PUNCT cana-3280 11 18 computer computer NOUN cana-3280 11 19 interaction interaction NOUN cana-3280 11 20 ( ( PUNCT cana-3280 11 21 hci hci NOUN cana-3280 11 22 ) ) PUNCT cana-3280 11 23 . . PUNCT cana-3280 12 1 two two NUM cana-3280 12 2 basic basic ADJ cana-3280 12 3 approaches approach NOUN cana-3280 12 4 exist exist VERB cana-3280 12 5 for for ADP cana-3280 12 6 activity activity NOUN cana-3280 12 7 recognition recognition NOUN cana-3280 12 8 : : PUNCT cana-3280 12 9 learning learn VERB cana-3280 12 10 - - PUNCT cana-3280 12 11 based base VERB cana-3280 12 12 representations representation NOUN cana-3280 12 13 and and CCONJ cana-3280 12 14 the the DET cana-3280 12 15 more more ADV cana-3280 12 16 traditional traditional ADJ cana-3280 12 17 , , PUNCT cana-3280 12 18 manually manually ADV cana-3280 12 19 produced produce VERB cana-3280 12 20 feature feature NOUN cana-3280 12 21 - - PUNCT cana-3280 12 22 based base VERB cana-3280 12 23 representation representation NOUN cana-3280 12 24 [ [ X cana-3280 12 25 1–4 1–4 NOUN cana-3280 12 26 ] ] X cana-3280 12 27 . . PUNCT cana-3280 13 1 the the DET cana-3280 13 2 learning learning NOUN cana-3280 13 3 - - PUNCT cana-3280 13 4 based base VERB cana-3280 13 5 representation representation NOUN cana-3280 13 6 , , PUNCT cana-3280 13 7 as as ADV cana-3280 13 8 well well ADV cana-3280 13 9 as as ADP cana-3280 13 10 particularly particularly ADV cana-3280 13 11 , , PUNCT cana-3280 13 12 deep deep ADJ cana-3280 13 13 learning learning NOUN cana-3280 13 14 , , PUNCT cana-3280 13 15 which which PRON cana-3280 13 16 included include VERB cana-3280 13 17 a a DET cana-3280 13 18 trainable trainable ADJ cana-3280 13 19 feature feature NOUN cana-3280 13 20 extractor extractor NOUN cana-3280 13 21 with with ADP cana-3280 13 22 a a DET cana-3280 13 23 trainable trainable ADJ cana-3280 13 24 classifier classifier NOUN cana-3280 13 25 , , PUNCT cana-3280 13 26 introduced introduce VERB cana-3280 13 27 the the DET cana-3280 13 28 idea idea NOUN cana-3280 13 29 of of ADP cana-3280 13 30 end end NOUN cana-3280 13 31 - - PUNCT cana-3280 13 32 to to ADP cana-3280 13 33 - - PUNCT cana-3280 13 34 end end NOUN cana-3280 13 35 learning learn VERB cana-3280 13 36 first first ADV cana-3280 13 37 . . PUNCT cana-3280 14 1 the the DET cana-3280 14 2 tremendous tremendous ADJ cana-3280 14 3 development development NOUN cana-3280 14 4 for for ADP cana-3280 14 5 action action NOUN cana-3280 14 6 recognition recognition NOUN cana-3280 14 7 in in ADP cana-3280 14 8 videos video NOUN cana-3280 14 9 has have AUX cana-3280 14 10 been be AUX cana-3280 14 11 disclosed disclose VERB cana-3280 14 12 by by ADP cana-3280 14 13 deep deep ADJ cana-3280 14 14 learning learning NOUN cana-3280 14 15 - - PUNCT cana-3280 14 16 based base VERB cana-3280 14 17 algorithms algorithm NOUN cana-3280 14 18 . . PUNCT cana-3280 15 1 for for ADP cana-3280 15 2 classifying classify VERB cana-3280 15 3 images image NOUN cana-3280 15 4 , , PUNCT cana-3280 15 5 recognizing recognize VERB cana-3280 15 6 objects object NOUN cana-3280 15 7 , , PUNCT cana-3280 15 8 and and CCONJ cana-3280 15 9 recognizing recognize VERB cana-3280 15 10 actions action NOUN cana-3280 15 11 , , PUNCT cana-3280 15 12 deep deep ADJ cana-3280 15 13 learning learning NOUN cana-3280 15 14 models model NOUN cana-3280 15 15 like like ADP cana-3280 15 16 cnn cnn PROPN cana-3280 16 1 [ [ X cana-3280 16 2 5 5 NUM cana-3280 16 3 ] ] PUNCT cana-3280 16 4 and and CCONJ cana-3280 16 5 deep deep ADJ cana-3280 16 6 belief belief NOUN cana-3280 16 7 networks network NOUN cana-3280 16 8 ( ( PUNCT cana-3280 16 9 dbns dbns PROPN cana-3280 16 10 ) ) PUNCT cana-3280 17 1 [ [ X cana-3280 17 2 6 6 NUM cana-3280 17 3 ] ] PUNCT cana-3280 17 4 were be AUX cana-3280 17 5 introduced introduce VERB cana-3280 17 6 to to PART cana-3280 17 7 reduce reduce VERB cana-3280 17 8 the the DET cana-3280 17 9 dimensionality dimensionality NOUN cana-3280 17 10 of of ADP cana-3280 17 11 the the DET cana-3280 17 12 input input NOUN cana-3280 17 13 . . PUNCT cana-3280 18 1 however however ADV cana-3280 18 2 , , PUNCT cana-3280 18 3 building build VERB cana-3280 18 4 a a DET cana-3280 18 5 new new ADJ cana-3280 18 6 deep deep ADJ cana-3280 18 7 learning learning NOUN cana-3280 18 8 model model NOUN cana-3280 18 9 from from ADP cana-3280 18 10 the the DET cana-3280 18 11 start start NOUN cana-3280 18 12 involves involve VERB cana-3280 18 13 a a DET cana-3280 18 14 significant significant ADJ cana-3280 18 15 quantity quantity NOUN cana-3280 18 16 of of ADP cana-3280 18 17 data datum NOUN cana-3280 18 18 , , PUNCT cana-3280 18 19 powerful powerful ADJ cana-3280 18 20 computing computing NOUN cana-3280 18 21 power power NOUN cana-3280 18 22 , , PUNCT cana-3280 18 23 and and CCONJ cana-3280 18 24 hours hour NOUN cana-3280 18 25 , , PUNCT cana-3280 18 26 or or CCONJ cana-3280 18 27 even even ADV cana-3280 18 28 days day NOUN cana-3280 18 29 , , PUNCT cana-3280 18 30 of of ADP cana-3280 18 31 training training NOUN cana-3280 18 32 [ [ X cana-3280 18 33 7 7 NUM cana-3280 18 34 - - SYM cana-3280 18 35 8 8 NUM cana-3280 18 36 ] ] PUNCT cana-3280 18 37 . . PUNCT cana-3280 19 1 in in ADP cana-3280 19 2 practical practical ADJ cana-3280 19 3 applications application NOUN cana-3280 19 4 , , PUNCT cana-3280 19 5 collecting collect VERB cana-3280 19 6 and and CCONJ cana-3280 19 7 annotating annotate VERB cana-3280 19 8 a a DET cana-3280 19 9 sizable sizable ADJ cana-3280 19 10 volume volume NOUN cana-3280 19 11 of of ADP cana-3280 19 12 domain domain NOUN cana-3280 19 13 - - PUNCT cana-3280 19 14 specific specific ADJ cana-3280 19 15 data datum NOUN cana-3280 19 16 requires require VERB cana-3280 19 17 a a DET cana-3280 19 18 lot lot NOUN cana-3280 19 19 of of ADP cana-3280 19 20 time time NOUN cana-3280 19 21 and and CCONJ cana-3280 19 22 money money NOUN cana-3280 19 23 . . PUNCT cana-3280 20 1 because because SCONJ cana-3280 20 2 it it PRON cana-3280 20 3 might might AUX cana-3280 20 4 not not PART cana-3280 20 5 always always ADV cana-3280 20 6 be be AUX cana-3280 20 7 possible possible ADJ cana-3280 20 8 to to PART cana-3280 20 9 collect collect VERB cana-3280 20 10 a a DET cana-3280 20 11 sizable sizable ADJ cana-3280 20 12 amount amount NOUN cana-3280 20 13 of of ADP cana-3280 20 14 domain domain NOUN cana-3280 20 15 - - PUNCT cana-3280 20 16 specific specific ADJ cana-3280 20 17 data datum NOUN cana-3280 20 18 , , PUNCT cana-3280 20 19 applying apply VERB cana-3280 20 20 deep deep ADJ cana-3280 20 21 learning learning NOUN cana-3280 20 22 models model NOUN cana-3280 20 23 can can AUX cana-3280 20 24 be be AUX cana-3280 20 25 challenging challenge VERB cana-3280 20 26 . . PUNCT cana-3280 21 1 to to PART cana-3280 21 2 solve solve VERB cana-3280 21 3 this this DET cana-3280 21 4 issue issue NOUN cana-3280 21 5 , , PUNCT cana-3280 21 6 researchers researcher NOUN cana-3280 21 7 changed change VERB cana-3280 21 8 the the DET cana-3280 21 9 way way NOUN cana-3280 21 10 they they PRON cana-3280 21 11 categorize categorize VERB cana-3280 21 12 images image NOUN cana-3280 21 13 so so SCONJ cana-3280 21 14 that that SCONJ cana-3280 21 15 they they PRON cana-3280 21 16 more more ADV cana-3280 21 17 closely closely ADV cana-3280 21 18 resemble resemble VERB cana-3280 21 19 how how SCONJ cana-3280 21 20 the the DET cana-3280 21 21 human human ADJ cana-3280 21 22 visual visual ADJ cana-3280 21 23 system system NOUN cana-3280 21 24 works work VERB cana-3280 21 25 . . PUNCT cana-3280 22 1 humans human NOUN cana-3280 22 2 can can AUX cana-3280 22 3 learn learn VERB cana-3280 22 4 numerous numerous ADJ cana-3280 22 5 categories category NOUN cana-3280 22 6 over over ADP cana-3280 22 7 their their PRON cana-3280 22 8 lifetimes lifetime NOUN cana-3280 22 9 from from ADP cana-3280 22 10 a a DET cana-3280 22 11 small small ADJ cana-3280 22 12 number number NOUN cana-3280 22 13 of of ADP cana-3280 22 14 samples sample NOUN cana-3280 22 15 . . PUNCT cana-3280 23 1 humans human NOUN cana-3280 23 2 are be AUX cana-3280 23 3 said say VERB cana-3280 23 4 to to PART cana-3280 23 5 have have AUX cana-3280 23 6 developed develop VERB cana-3280 23 7 this this DET cana-3280 23 8 skill skill NOUN cana-3280 23 9 through through ADP cana-3280 23 10 amassing amass VERB cana-3280 23 11 information information NOUN cana-3280 23 12 over over ADP cana-3280 23 13 time time NOUN cana-3280 23 14 and and CCONJ cana-3280 23 15 using use VERB cana-3280 23 16 it it PRON cana-3280 23 17 to to PART cana-3280 23 18 learn learn VERB cana-3280 23 19 new new ADJ cana-3280 23 20 things thing NOUN cana-3280 23 21 [ [ X cana-3280 23 22 9 9 NUM cana-3280 23 23 - - SYM cana-3280 23 24 10 10 NUM cana-3280 23 25 ] ] PUNCT cana-3280 23 26 . . PUNCT cana-3280 24 1 according accord VERB cana-3280 24 2 to to ADP cana-3280 24 3 researchers researcher NOUN cana-3280 24 4 , , PUNCT cana-3280 24 5 understanding understand VERB cana-3280 24 6 earlier early ADJ cana-3280 24 7 things thing NOUN cana-3280 24 8 helps help VERB cana-3280 24 9 with with ADP cana-3280 24 10 learning learn VERB cana-3280 24 11 new new ADJ cana-3280 24 12 ones one NOUN cana-3280 24 13 because because SCONJ cana-3280 24 14 of of ADP cana-3280 24 15 how how SCONJ cana-3280 24 16 they they PRON cana-3280 24 17 are be AUX cana-3280 24 18 related relate VERB cana-3280 24 19 to to ADP cana-3280 24 20 and and CCONJ cana-3280 24 21 similar similar ADJ cana-3280 24 22 to to ADP cana-3280 24 23 the the DET cana-3280 24 24 new new ADJ cana-3280 24 25 ones one NOUN cana-3280 24 26 . . PUNCT cana-3280 25 1 studies study NOUN cana-3280 25 2 have have AUX cana-3280 25 3 shown show VERB cana-3280 25 4 that that SCONJ cana-3280 25 5 deep deep ADJ cana-3280 25 6 learning learning NOUN cana-3280 25 7 models model NOUN cana-3280 25 8 which which PRON cana-3280 25 9 have have AUX cana-3280 25 10 been be AUX cana-3280 25 11 trained train VERB cana-3280 25 12 for for ADP cana-3280 25 13 a a DET cana-3280 25 14 single single ADJ cana-3280 25 15 classification classification NOUN cana-3280 25 16 task task NOUN cana-3280 25 17 may may AUX cana-3280 25 18 be be AUX cana-3280 25 19 used use VERB cana-3280 25 20 for for ADP cana-3280 25 21 numerous numerous ADJ cana-3280 25 22 classification classification NOUN cana-3280 25 23 challenges challenge NOUN cana-3280 25 24 . . PUNCT cana-3280 26 1 cnn cnn PROPN cana-3280 26 2 models model NOUN cana-3280 26 3 that that PRON cana-3280 26 4 have have AUX cana-3280 26 5 been be AUX cana-3280 26 6 trained train VERB cana-3280 26 7 on on ADP cana-3280 26 8 a a DET cana-3280 26 9 certain certain ADJ cana-3280 26 10 dataset dataset NOUN cana-3280 26 11 or or CCONJ cana-3280 26 12 task task NOUN cana-3280 26 13 can can AUX cana-3280 26 14 therefore therefore ADV cana-3280 26 15 be be AUX cana-3280 26 16 modified modify VERB cana-3280 26 17 for for ADP cana-3280 26 18 a a DET cana-3280 26 19 new new ADJ cana-3280 26 20 task task NOUN cana-3280 26 21 in in ADP cana-3280 26 22 a a DET cana-3280 26 23 different different ADJ cana-3280 26 24 domain domain NOUN cana-3280 26 25 . . PUNCT cana-3280 27 1 mailto mailto PROPN cana-3280 27 2 : : PUNCT cana-3280 27 3 saurabhg1@srmist.edu.in1 saurabhg1@srmist.edu.in1 VERB cana-3280 27 4 mailto:rajendram@srmist.edu.in mailto:rajendram@srmist.edu.in X cana-3280 27 5 communications communication NOUN cana-3280 27 6 on on ADP cana-3280 27 7 applied apply VERB cana-3280 27 8 nonlinear nonlinear ADJ cana-3280 27 9 analysis analysis NOUN cana-3280 27 10 issn issn NOUN cana-3280 27 11 : : PUNCT cana-3280 27 12 1074 1074 NUM cana-3280 27 13 - - PUNCT cana-3280 27 14 133x 133x NUM cana-3280 27 15 vol vol NOUN cana-3280 27 16 32 32 NUM cana-3280 27 17 no no NOUN cana-3280 27 18 . . PUNCT cana-3280 28 1 6s 6s NUM cana-3280 28 2 ( ( PUNCT cana-3280 28 3 2025 2025 NUM cana-3280 28 4 ) ) PUNCT cana-3280 28 5 124 124 NUM cana-3280 28 6 https://internationalpubls.com https://internationalpubls.com X cana-3280 28 7 this this DET cana-3280 28 8 concept concept NOUN cana-3280 28 9 is be AUX cana-3280 28 10 referred refer VERB cana-3280 28 11 to to ADP cana-3280 28 12 as as ADP cana-3280 28 13 domain domain NOUN cana-3280 28 14 adaptation adaptation NOUN cana-3280 28 15 or or CCONJ cana-3280 28 16 transfer transfer NOUN cana-3280 28 17 learning learning NOUN cana-3280 28 18 . . PUNCT cana-3280 29 1 [ [ X cana-3280 29 2 11]transfer 11]transfer NUM cana-3280 29 3 learning learn VERB cana-3280 29 4 is be AUX cana-3280 29 5 a a DET cana-3280 29 6 machine machine NOUN cana-3280 29 7 learning learning NOUN cana-3280 29 8 technique technique NOUN cana-3280 29 9 that that PRON cana-3280 29 10 has have AUX cana-3280 29 11 long long ADV cana-3280 29 12 been be AUX cana-3280 29 13 researched research VERB cana-3280 29 14 for for ADP cana-3280 29 15 addressing address VERB cana-3280 29 16 various various ADJ cana-3280 29 17 visual visual ADJ cana-3280 29 18 classification classification NOUN cana-3280 29 19 problems problem NOUN cana-3280 29 20 . . PUNCT cana-3280 30 1 the the DET cana-3280 30 2 current current ADJ cana-3280 30 3 growth growth NOUN cana-3280 30 4 of of ADP cana-3280 30 5 information information NOUN cana-3280 30 6 , , PUNCT cana-3280 30 7 including include VERB cana-3280 30 8 images image NOUN cana-3280 30 9 , , PUNCT cana-3280 30 10 sounds sound VERB cana-3280 30 11 , , PUNCT cana-3280 30 12 and and CCONJ cana-3280 30 13 videos video NOUN cana-3280 30 14 , , PUNCT cana-3280 30 15 available available ADJ cana-3280 30 16 online online ADV cana-3280 30 17 has have AUX cana-3280 30 18 increased increase VERB cana-3280 30 19 the the DET cana-3280 30 20 demand demand NOUN cana-3280 30 21 for for ADP cana-3280 30 22 high high ADJ cana-3280 30 23 accuracy accuracy NOUN cana-3280 30 24 and and CCONJ cana-3280 30 25 computing computing NOUN cana-3280 30 26 efficiency efficiency NOUN cana-3280 30 27 [ [ X cana-3280 30 28 12 12 NUM cana-3280 30 29 ] ] PUNCT cana-3280 30 30 . . PUNCT cana-3280 31 1 these these DET cana-3280 31 2 reasons reason NOUN cana-3280 31 3 have have AUX cana-3280 31 4 contributed contribute VERB cana-3280 31 5 to to ADP cana-3280 31 6 the the DET cana-3280 31 7 widespread widespread ADJ cana-3280 31 8 use use NOUN cana-3280 31 9 of of ADP cana-3280 31 10 transfer transfer NOUN cana-3280 31 11 learning learning NOUN cana-3280 31 12 in in ADP cana-3280 31 13 the the DET cana-3280 31 14 domains domain NOUN cana-3280 31 15 of of ADP cana-3280 31 16 machine machine NOUN cana-3280 31 17 learning learning NOUN cana-3280 31 18 and and CCONJ cana-3280 31 19 computer computer NOUN cana-3280 31 20 vision vision NOUN cana-3280 31 21 . . PUNCT cana-3280 32 1 once once SCONJ cana-3280 32 2 conventional conventional ADJ cana-3280 32 3 machine machine NOUN cana-3280 32 4 learning learning NOUN cana-3280 32 5 algorithms algorithm NOUN cana-3280 32 6 have have AUX cana-3280 32 7 reached reach VERB cana-3280 32 8 their their PRON cana-3280 32 9 limits limit NOUN cana-3280 32 10 , , PUNCT cana-3280 32 11 transfer transfer NOUN cana-3280 32 12 learning learning NOUN cana-3280 32 13 opens open VERB cana-3280 32 14 up up ADP cana-3280 32 15 new new ADJ cana-3280 32 16 possibilities possibility NOUN cana-3280 32 17 for for ADP cana-3280 32 18 visual visual ADJ cana-3280 32 19 classification classification NOUN cana-3280 32 20 . . PUNCT cana-3280 33 1 it it PRON cana-3280 33 2 has have AUX cana-3280 33 3 mostly mostly ADV cana-3280 33 4 changed change VERB cana-3280 33 5 the the DET cana-3280 33 6 process process NOUN cana-3280 33 7 that that PRON cana-3280 33 8 machines machine NOUN cana-3280 33 9 utilize utilize VERB cana-3280 33 10 to to PART cana-3280 33 11 learn learn VERB cana-3280 33 12 and and CCONJ cana-3280 33 13 handle handle VERB cana-3280 33 14 classification classification NOUN cana-3280 33 15 tasks task NOUN cana-3280 33 16 [ [ X cana-3280 33 17 13 13 NUM cana-3280 33 18 ] ] PUNCT cana-3280 33 19 . . PUNCT cana-3280 34 1 it it PRON cana-3280 34 2 has have AUX cana-3280 34 3 been be AUX cana-3280 34 4 utilized utilize VERB cana-3280 34 5 successfully successfully ADV cana-3280 34 6 for for ADP cana-3280 34 7 visual visual ADJ cana-3280 34 8 categorization categorization NOUN cana-3280 34 9 tasks task NOUN cana-3280 34 10 in in ADP cana-3280 34 11 the the DET cana-3280 34 12 fields field NOUN cana-3280 34 13 of of ADP cana-3280 34 14 recognizing recognize VERB cana-3280 34 15 objects object NOUN cana-3280 34 16 , , PUNCT cana-3280 34 17 image image NOUN cana-3280 34 18 classification classification NOUN cana-3280 34 19 , , PUNCT cana-3280 34 20 or or CCONJ cana-3280 34 21 human human ADJ cana-3280 34 22 activity activity NOUN cana-3280 34 23 recognition recognition NOUN cana-3280 34 24 . . PUNCT cana-3280 35 1 transfer transfer NOUN cana-3280 35 2 learning learning NOUN cana-3280 35 3 often often ADV cana-3280 35 4 employs employ VERB cana-3280 35 5 two two NUM cana-3280 35 6 techniques technique NOUN cana-3280 35 7 : : PUNCT cana-3280 35 8 1 1 X cana-3280 35 9 ) ) PUNCT cana-3280 35 10 keeping keep VERB cana-3280 35 11 the the DET cana-3280 35 12 pre pre ADJ cana-3280 35 13 - - ADJ cana-3280 35 14 trained trained ADJ cana-3280 35 15 network network NOUN cana-3280 35 16 in in ADP cana-3280 35 17 place place NOUN cana-3280 35 18 while while SCONJ cana-3280 35 19 changing change VERB cana-3280 35 20 the the DET cana-3280 35 21 weights weight NOUN cana-3280 35 22 in in ADP cana-3280 35 23 response response NOUN cana-3280 35 24 to to ADP cana-3280 35 25 fresh fresh ADJ cana-3280 35 26 training training NOUN cana-3280 35 27 data datum NOUN cana-3280 35 28 . . PUNCT cana-3280 36 1 2 2 X cana-3280 36 2 ) ) PUNCT cana-3280 36 3 using use VERB cana-3280 36 4 a a DET cana-3280 36 5 pre pre ADJ cana-3280 36 6 - - ADJ cana-3280 36 7 trained trained ADJ cana-3280 36 8 network network NOUN cana-3280 36 9 for for ADP cana-3280 36 10 feature feature NOUN cana-3280 36 11 extraction extraction NOUN cana-3280 36 12 with with ADP cana-3280 36 13 representation representation NOUN cana-3280 36 14 and and CCONJ cana-3280 36 15 a a DET cana-3280 36 16 general general ADJ cana-3280 36 17 classifier classifier NOUN cana-3280 36 18 , , PUNCT cana-3280 36 19 like like ADP cana-3280 36 20 svm svm PROPN cana-3280 36 21 , , PUNCT cana-3280 36 22 for for ADP cana-3280 36 23 classification classification NOUN cana-3280 36 24 [ [ X cana-3280 36 25 14 14 NUM cana-3280 36 26 - - SYM cana-3280 36 27 15 15 NUM cana-3280 36 28 ] ] PUNCT cana-3280 36 29 . . PUNCT cana-3280 37 1 numerous numerous ADJ cana-3280 37 2 tasks task NOUN cana-3280 37 3 involving involve VERB cana-3280 37 4 recognition recognition NOUN cana-3280 37 5 and and CCONJ cana-3280 37 6 categorization categorization NOUN cana-3280 37 7 have have AUX cana-3280 37 8 seen see VERB cana-3280 37 9 success success NOUN cana-3280 37 10 using use VERB cana-3280 37 11 the the DET cana-3280 37 12 second second ADJ cana-3280 37 13 strategy strategy NOUN cana-3280 37 14 . . PUNCT cana-3280 38 1 the the DET cana-3280 38 2 second second ADJ cana-3280 38 3 group group NOUN cana-3280 38 4 also also ADV cana-3280 38 5 applies apply VERB cana-3280 38 6 to to ADP cana-3280 38 7 our our PRON cana-3280 38 8 suggested suggest VERB cana-3280 38 9 method method NOUN cana-3280 38 10 for for ADP cana-3280 38 11 identifying identify VERB cana-3280 38 12 human human ADJ cana-3280 38 13 action action NOUN cana-3280 38 14 . . PUNCT cana-3280 39 1 we we PRON cana-3280 39 2 looked look VERB cana-3280 39 3 into into ADP cana-3280 39 4 recently recently ADV cana-3280 39 5 put put VERB cana-3280 39 6 forward forward ADV cana-3280 39 7 benchmark benchmark NOUN cana-3280 39 8 deep deep ADJ cana-3280 39 9 model model NOUN cana-3280 39 10 examples example NOUN cana-3280 39 11 like like ADP cana-3280 39 12 alex alex PROPN cana-3280 39 13 net net PROPN cana-3280 39 14 and and CCONJ cana-3280 39 15 google google PROPN cana-3280 39 16 net net NOUN cana-3280 39 17 . . PUNCT cana-3280 40 1 using use VERB cana-3280 40 2 the the DET cana-3280 40 3 results result NOUN cana-3280 40 4 of of ADP cana-3280 40 5 the the DET cana-3280 40 6 studies study NOUN cana-3280 40 7 , , PUNCT cana-3280 40 8 2dcnn 2dcnn NUM cana-3280 40 9 and and CCONJ cana-3280 40 10 lrcn lrcn PROPN cana-3280 40 11 were be AUX cana-3280 40 12 selected select VERB cana-3280 40 13 as as ADP cana-3280 40 14 the the DET cana-3280 40 15 sources source NOUN cana-3280 40 16 for for ADP cana-3280 40 17 a a DET cana-3280 40 18 target target NOUN cana-3280 40 19 model model NOUN cana-3280 40 20 that that PRON cana-3280 40 21 would would AUX cana-3280 40 22 be be AUX cana-3280 40 23 used use VERB cana-3280 40 24 to to PART cana-3280 40 25 recognize recognize VERB cana-3280 40 26 actions action NOUN cana-3280 40 27 [ [ X cana-3280 40 28 16 16 NUM cana-3280 40 29 ] ] PUNCT cana-3280 40 30 . . PUNCT cana-3280 41 1 a a DET cana-3280 41 2 2d 2d NUM cana-3280 41 3 cnnlstm cnnlstm NOUN cana-3280 41 4 classifier classifier NOUN cana-3280 41 5 hybrid hybrid NOUN cana-3280 41 6 was be AUX cana-3280 41 7 employed employ VERB cana-3280 41 8 for for ADP cana-3280 41 9 feature feature NOUN cana-3280 41 10 extraction extraction NOUN cana-3280 41 11 while while SCONJ cana-3280 41 12 the the DET cana-3280 41 13 representation representation NOUN cana-3280 41 14 using use VERB cana-3280 41 15 the the DET cana-3280 41 16 source source NOUN cana-3280 41 17 model model NOUN cana-3280 41 18 , , PUNCT cana-3280 41 19 and and CCONJ cana-3280 41 20 the the DET cana-3280 41 21 source source NOUN cana-3280 41 22 model model NOUN cana-3280 41 23 were be AUX cana-3280 41 24 subsequently subsequently ADV cana-3280 41 25 used use VERB cana-3280 41 26 for for ADP cana-3280 41 27 action action NOUN cana-3280 41 28 recognition recognition NOUN cana-3280 41 29 . . PUNCT cana-3280 42 1 2 2 X cana-3280 42 2 . . X cana-3280 42 3 related relate VERB cana-3280 42 4 work work NOUN cana-3280 42 5 hejazi hejazi NOUN cana-3280 42 6 2022 2022 NUM cana-3280 42 7 et et PROPN cana-3280 42 8 al al PROPN cana-3280 42 9 . . PUNCT cana-3280 43 1 this this DET cana-3280 43 2 work work NOUN cana-3280 43 3 seeks seek VERB cana-3280 43 4 to to PART cana-3280 43 5 recognize recognize VERB cana-3280 43 6 human human ADJ cana-3280 43 7 behaviours behaviour NOUN cana-3280 43 8 by by ADP cana-3280 43 9 generating generate VERB cana-3280 43 10 manually manually ADV cana-3280 43 11 constructed construct VERB cana-3280 43 12 features feature NOUN cana-3280 43 13 using use VERB cana-3280 43 14 phase phase NOUN cana-3280 43 15 data datum NOUN cana-3280 43 16 recovered recover VERB cana-3280 43 17 from from ADP cana-3280 43 18 the the DET cana-3280 43 19 frequency frequency NOUN cana-3280 43 20 domain domain NOUN cana-3280 43 21 on on ADP cana-3280 43 22 the the DET cana-3280 43 23 video video NOUN cana-3280 43 24 data datum NOUN cana-3280 43 25 . . PUNCT cana-3280 44 1 we we PRON cana-3280 44 2 wish wish VERB cana-3280 44 3 to to PART cana-3280 44 4 explain explain VERB cana-3280 44 5 the the DET cana-3280 44 6 motion motion NOUN cana-3280 44 7 dynamics dynamic NOUN cana-3280 44 8 of of ADP cana-3280 44 9 the the DET cana-3280 44 10 scene scene NOUN cana-3280 44 11 using use VERB cana-3280 44 12 localized localize VERB cana-3280 44 13 phase phase NOUN cana-3280 44 14 information information NOUN cana-3280 44 15 rather rather ADV cana-3280 44 16 than than ADP cana-3280 44 17 calculating calculate VERB cana-3280 44 18 motion motion NOUN cana-3280 44 19 vectors vector NOUN cana-3280 44 20 or or CCONJ cana-3280 44 21 estimating estimate VERB cana-3280 44 22 optical optical ADJ cana-3280 44 23 flow flow NOUN cana-3280 44 24 . . PUNCT cana-3280 45 1 phase phase NOUN cana-3280 45 2 correlation correlation NOUN cana-3280 45 3 data datum NOUN cana-3280 45 4 from from ADP cana-3280 45 5 each each PRON cana-3280 45 6 of of ADP cana-3280 45 7 the the DET cana-3280 45 8 next next ADJ cana-3280 45 9 two two NUM cana-3280 45 10 frames frame NOUN cana-3280 45 11 of of ADP cana-3280 45 12 each each PRON cana-3280 45 13 of of ADP cana-3280 45 14 the the DET cana-3280 45 15 video video NOUN cana-3280 45 16 recordings recording NOUN cana-3280 45 17 ' ' PART cana-3280 45 18 two two NUM cana-3280 45 19 consecutive consecutive ADJ cana-3280 45 20 frames frame NOUN cana-3280 45 21 were be AUX cana-3280 45 22 used use VERB cana-3280 45 23 to to PART cana-3280 45 24 train train VERB cana-3280 45 25 a a DET cana-3280 45 26 model model NOUN cana-3280 45 27 to to PART cana-3280 45 28 represent represent VERB cana-3280 45 29 the the DET cana-3280 45 30 action action NOUN cana-3280 45 31 . . PUNCT cana-3280 46 1 we we PRON cana-3280 46 2 have have AUX cana-3280 46 3 worked work VERB cana-3280 46 4 very very ADV cana-3280 46 5 hard hard ADV cana-3280 46 6 to to PART cana-3280 46 7 evaluate evaluate VERB cana-3280 46 8 the the DET cana-3280 46 9 performance performance NOUN cana-3280 46 10 of of ADP cana-3280 46 11 our our PRON cana-3280 46 12 method method NOUN cana-3280 46 13 on on ADP cana-3280 46 14 three three NUM cana-3280 46 15 huge huge ADJ cana-3280 46 16 and and CCONJ cana-3280 46 17 complex complex ADJ cana-3280 46 18 datasets dataset NOUN cana-3280 46 19 : : PUNCT cana-3280 46 20 ucf101 ucf101 NOUN cana-3280 46 21 , , PUNCT cana-3280 46 22 kinetics-400 kinetics-400 NOUN cana-3280 46 23 , , PUNCT cana-3280 46 24 and and CCONJ cana-3280 46 25 kinetics-700 kinetics-700 NOUN cana-3280 46 26 . . PUNCT cana-3280 47 1 the the DET cana-3280 47 2 results result NOUN cana-3280 47 3 show show VERB cana-3280 47 4 that that SCONJ cana-3280 47 5 , , PUNCT cana-3280 47 6 over over ADP cana-3280 47 7 all all PRON cana-3280 47 8 of of ADP cana-3280 47 9 these these DET cana-3280 47 10 datasets dataset NOUN cana-3280 47 11 , , PUNCT cana-3280 47 12 our our PRON cana-3280 47 13 technique technique NOUN cana-3280 47 14 is be AUX cana-3280 47 15 very very ADV cana-3280 47 16 accurate accurate ADJ cana-3280 47 17 at at ADP cana-3280 47 18 detecting detect VERB cana-3280 47 19 human human ADJ cana-3280 47 20 actions action NOUN cana-3280 47 21 [ [ X cana-3280 47 22 17 17 NUM cana-3280 47 23 ] ] PUNCT cana-3280 47 24 . . PUNCT cana-3280 48 1 cui cui VERB cana-3280 48 2 2022 2022 NUM cana-3280 48 3 et et PROPN cana-3280 48 4 al al PROPN cana-3280 48 5 . . PUNCT cana-3280 49 1 to to PART cana-3280 49 2 lessen lessen VERB cana-3280 49 3 the the DET cana-3280 49 4 pressure pressure NOUN cana-3280 49 5 on on ADP cana-3280 49 6 the the DET cana-3280 49 7 primary primary ADJ cana-3280 49 8 cloud cloud NOUN cana-3280 49 9 server server NOUN cana-3280 49 10 , , PUNCT cana-3280 49 11 the the DET cana-3280 49 12 transmission transmission NOUN cana-3280 49 13 load load NOUN cana-3280 49 14 , , PUNCT cana-3280 49 15 and and CCONJ cana-3280 49 16 the the DET cana-3280 49 17 processing processing NOUN cana-3280 49 18 latency latency NOUN cana-3280 49 19 of of ADP cana-3280 49 20 the the DET cana-3280 49 21 video video NOUN cana-3280 49 22 surveillance surveillance NOUN cana-3280 49 23 system system NOUN cana-3280 49 24 , , PUNCT cana-3280 49 25 a a DET cana-3280 49 26 distributed distribute VERB cana-3280 49 27 computing computing NOUN cana-3280 49 28 design design NOUN cana-3280 49 29 is be AUX cana-3280 49 30 developed develop VERB cana-3280 49 31 that that PRON cana-3280 49 32 directly directly ADV cana-3280 49 33 handles handle VERB cana-3280 49 34 peripherals peripheral NOUN cana-3280 49 35 ' ' PART cana-3280 49 36 video video NOUN cana-3280 49 37 data datum NOUN cana-3280 49 38 . . PUNCT cana-3280 50 1 a a DET cana-3280 50 2 lightweight lightweight ADJ cana-3280 50 3 neural neural ADJ cana-3280 50 4 network network NOUN cana-3280 50 5 model model NOUN cana-3280 50 6 and and CCONJ cana-3280 50 7 the the DET cana-3280 50 8 federated federated ADJ cana-3280 50 9 learning learning NOUN cana-3280 50 10 strategy strategy NOUN cana-3280 50 11 are be AUX cana-3280 50 12 both both PRON cana-3280 50 13 advised advise VERB cana-3280 50 14 . . PUNCT cana-3280 51 1 for for ADP cana-3280 51 2 a a DET cana-3280 51 3 variety variety NOUN cana-3280 51 4 of of ADP cana-3280 51 5 circumstances circumstance NOUN cana-3280 51 6 , , PUNCT cana-3280 51 7 computation computation NOUN cana-3280 51 8 models model NOUN cana-3280 51 9 are be AUX cana-3280 51 10 built build VERB cana-3280 51 11 using use VERB cana-3280 51 12 light light ADJ cana-3280 51 13 neural neural ADJ cana-3280 51 14 network network NOUN cana-3280 51 15 technology technology NOUN cana-3280 51 16 , , PUNCT cana-3280 51 17 and and CCONJ cana-3280 51 18 the the DET cana-3280 51 19 produced produce VERB cana-3280 51 20 models model NOUN cana-3280 51 21 are be AUX cana-3280 51 22 logically logically ADV cana-3280 51 23 structured structure VERB cana-3280 51 24 in in ADP cana-3280 51 25 edge edge NOUN cana-3280 51 26 devices device NOUN cana-3280 51 27 . . PUNCT cana-3280 52 1 human human ADJ cana-3280 52 2 motion motion NOUN cana-3280 52 3 analysis analysis NOUN cana-3280 52 4 has have AUX cana-3280 52 5 developed develop VERB cana-3280 52 6 into into ADP cana-3280 52 7 a a DET cana-3280 52 8 significant significant ADJ cana-3280 52 9 research research NOUN cana-3280 52 10 area area NOUN cana-3280 52 11 as as ADP cana-3280 52 12 technology technology NOUN cana-3280 52 13 advances advance NOUN cana-3280 52 14 . . PUNCT cana-3280 53 1 this this DET cana-3280 53 2 analysis analysis NOUN cana-3280 53 3 makes make VERB cana-3280 53 4 it it PRON cana-3280 53 5 possible possible ADJ cana-3280 53 6 for for SCONJ cana-3280 53 7 people people NOUN cana-3280 53 8 to to PART cana-3280 53 9 recognize recognize VERB cana-3280 53 10 motion motion NOUN cana-3280 53 11 . . PUNCT cana-3280 54 1 chinese chinese ADJ cana-3280 54 2 taekwondo taekwondo NOUN cana-3280 54 3 is be AUX cana-3280 54 4 now now ADV cana-3280 54 5 of of ADP cana-3280 54 6 higher high ADJ cana-3280 54 7 quality quality NOUN cana-3280 54 8 than than SCONJ cana-3280 54 9 it it PRON cana-3280 54 10 was be AUX cana-3280 54 11 in in ADP cana-3280 54 12 earlier early ADJ cana-3280 54 13 eras era NOUN cana-3280 54 14 due due ADP cana-3280 54 15 to to ADP cana-3280 54 16 the the DET cana-3280 54 17 growth growth NOUN cana-3280 54 18 of of ADP cana-3280 54 19 society society NOUN cana-3280 54 20 . . PUNCT cana-3280 55 1 since since SCONJ cana-3280 55 2 then then ADV cana-3280 55 3 , , PUNCT cana-3280 55 4 the the DET cana-3280 55 5 number number NOUN cana-3280 55 6 of of ADP cana-3280 55 7 chinese chinese ADJ cana-3280 55 8 taekwondo taekwondo NOUN cana-3280 55 9 classes class NOUN cana-3280 55 10 has have AUX cana-3280 55 11 gradually gradually ADV cana-3280 55 12 expanded expand VERB cana-3280 55 13 , , PUNCT cana-3280 55 14 however however ADV cana-3280 55 15 , , PUNCT cana-3280 55 16 the the DET cana-3280 55 17 issue issue NOUN cana-3280 55 18 with with ADP cana-3280 55 19 inconsistent inconsistent ADJ cana-3280 55 20 instruction instruction NOUN cana-3280 55 21 quality quality NOUN cana-3280 55 22 is be AUX cana-3280 55 23 to to PART cana-3280 55 24 blame blame VERB cana-3280 55 25 . . PUNCT cana-3280 56 1 the the DET cana-3280 56 2 author author NOUN cana-3280 56 3 conducted conduct VERB cana-3280 56 4 specific specific ADJ cana-3280 56 5 research research NOUN cana-3280 56 6 using use VERB cana-3280 56 7 d.a d.a PROPN cana-3280 56 8 . . PROPN cana-3280 56 9 cooper cooper PROPN cana-3280 56 10 's 's PART cana-3280 56 11 empirical empirical ADJ cana-3280 56 12 theory theory NOUN cana-3280 56 13 of of ADP cana-3280 56 14 learning learn VERB cana-3280 56 15 to to PART cana-3280 56 16 help help VERB cana-3280 56 17 with with ADP cana-3280 56 18 overcoming overcome VERB cana-3280 56 19 the the DET cana-3280 56 20 teaching teaching NOUN cana-3280 56 21 issues issue NOUN cana-3280 56 22 in in ADP cana-3280 56 23 the the DET cana-3280 56 24 taekwondo taekwondo NOUN cana-3280 56 25 learning learning NOUN cana-3280 56 26 process process NOUN cana-3280 56 27 and and CCONJ cana-3280 56 28 raise raise VERB cana-3280 56 29 the the DET cana-3280 56 30 standard standard NOUN cana-3280 56 31 of of ADP cana-3280 56 32 chinese chinese ADJ cana-3280 56 33 taekwondo taekwondo NOUN cana-3280 56 34 . . PUNCT cana-3280 57 1 further further ADJ cana-3280 57 2 investigation investigation NOUN cana-3280 57 3 will will AUX cana-3280 57 4 be be AUX cana-3280 57 5 conducted conduct VERB cana-3280 57 6 to to PART cana-3280 57 7 meet meet VERB cana-3280 57 8 real real ADJ cana-3280 57 9 - - PUNCT cana-3280 57 10 time time NOUN cana-3280 57 11 needs need NOUN cana-3280 57 12 , , PUNCT cana-3280 57 13 including include VERB cana-3280 57 14 the the DET cana-3280 57 15 development development NOUN cana-3280 57 16 of of ADP cana-3280 57 17 neural neural ADJ cana-3280 57 18 networks network NOUN cana-3280 57 19 for for ADP cana-3280 57 20 compression compression NOUN cana-3280 57 21 acceleration acceleration NOUN cana-3280 57 22 algorithms algorithm NOUN cana-3280 57 23 , , PUNCT cana-3280 57 24 techniques technique NOUN cana-3280 57 25 for for ADP cana-3280 57 26 universal universal ADJ cana-3280 57 27 neural neural ADJ cana-3280 57 28 networks network NOUN cana-3280 57 29 with with ADP cana-3280 57 30 weight weight NOUN cana-3280 57 31 updates update NOUN cana-3280 57 32 , , PUNCT cana-3280 57 33 finding find VERB cana-3280 57 34 from from ADP cana-3280 57 35 detection detection NOUN cana-3280 57 36 , , PUNCT cana-3280 57 37 or or CCONJ cana-3280 57 38 federated federated ADJ cana-3280 57 39 learning learning NOUN cana-3280 57 40 algorithms algorithm NOUN cana-3280 57 41 . . PUNCT cana-3280 58 1 this this DET cana-3280 58 2 study study NOUN cana-3280 58 3 makes make VERB cana-3280 58 4 use use NOUN cana-3280 58 5 of of ADP cana-3280 58 6 scientific scientific ADJ cana-3280 58 7 and and CCONJ cana-3280 58 8 technological technological ADJ cana-3280 58 9 approaches approach NOUN cana-3280 58 10 to to PART cana-3280 58 11 investigate investigate VERB cana-3280 58 12 technical technical ADJ cana-3280 58 13 actions action NOUN cana-3280 58 14 and and CCONJ cana-3280 58 15 strategies strategy NOUN cana-3280 58 16 . . PUNCT cana-3280 59 1 then then ADV cana-3280 59 2 , , PUNCT cana-3280 59 3 these these DET cana-3280 59 4 techniques technique NOUN cana-3280 59 5 are be AUX cana-3280 59 6 used use VERB cana-3280 59 7 for for ADP cana-3280 59 8 specific specific ADJ cana-3280 59 9 educational educational ADJ cana-3280 59 10 experimentation experimentation NOUN cana-3280 59 11 and and CCONJ cana-3280 59 12 assessment assessment NOUN cana-3280 59 13 [ [ X cana-3280 59 14 18].mar 18].mar PROPN cana-3280 59 15 - - NOUN cana-3280 59 16 cupido cupido ADJ cana-3280 59 17 2022 2022 NUM cana-3280 59 18 et et PROPN cana-3280 59 19 al al PROPN cana-3280 59 20 . . PUNCT cana-3280 60 1 use use VERB cana-3280 60 2 four four NUM cana-3280 60 3 pre pre ADJ cana-3280 60 4 - - ADJ cana-3280 60 5 trained train VERB cana-3280 60 6 deep deep ADJ cana-3280 60 7 learning learning NOUN cana-3280 60 8 algorithms algorithm NOUN cana-3280 60 9 ( ( PUNCT cana-3280 60 10 nas nas PROPN cana-3280 60 11 net net PROPN cana-3280 60 12 mobile mobile NOUN cana-3280 60 13 , , PUNCT cana-3280 60 14 mobilenetv2 mobilenetv2 PROPN cana-3280 60 15 , , PUNCT cana-3280 60 16 resnet101v2 resnet101v2 NOUN cana-3280 60 17 , , PUNCT cana-3280 60 18 & & CCONJ cana-3280 60 19 resnet152v2 resnet152v2 PROPN cana-3280 60 20 ) ) PUNCT cana-3280 60 21 to to PART cana-3280 60 22 categorize categorize VERB cana-3280 60 23 photographs photograph NOUN cana-3280 60 24 according accord VERB cana-3280 60 25 to to ADP cana-3280 60 26 the the DET cana-3280 60 27 kind kind NOUN cana-3280 60 28 of of ADP cana-3280 60 29 face face NOUN cana-3280 60 30 masks mask NOUN cana-3280 60 31 ( ( PUNCT cana-3280 60 32 kn95 kn95 PROPN cana-3280 60 33 , , PUNCT cana-3280 60 34 n95 n95 ADJ cana-3280 60 35 , , PUNCT cana-3280 60 36 surgical surgical ADJ cana-3280 60 37 , , PUNCT cana-3280 60 38 and and CCONJ cana-3280 60 39 fabric fabric NOUN cana-3280 60 40 ) ) PUNCT cana-3280 60 41 worn wear VERB cana-3280 60 42 by by ADP cana-3280 60 43 people people NOUN cana-3280 60 44 in in ADP cana-3280 60 45 the the DET cana-3280 60 46 pictures picture NOUN cana-3280 60 47 . . PUNCT cana-3280 61 1 deep deep ADJ cana-3280 61 2 residual residual ADJ cana-3280 61 3 network network NOUN cana-3280 61 4 models model NOUN cana-3280 61 5 ( ( PUNCT cana-3280 61 6 resnet101v2 resnet101v2 NOUN cana-3280 61 7 and and CCONJ cana-3280 61 8 resnet152v2 resnet152v2 NOUN cana-3280 61 9 ) ) PUNCT cana-3280 61 10 provide provide VERB cana-3280 61 11 the the DET cana-3280 61 12 communications communication NOUN cana-3280 61 13 on on ADP cana-3280 61 14 applied apply VERB cana-3280 61 15 nonlinear nonlinear ADJ cana-3280 61 16 analysis analysis NOUN cana-3280 61 17 issn issn NOUN cana-3280 61 18 : : PUNCT cana-3280 61 19 1074 1074 NUM cana-3280 61 20 - - PUNCT cana-3280 61 21 133x 133x NUM cana-3280 61 22 vol vol NOUN cana-3280 61 23 32 32 NUM cana-3280 61 24 no no NOUN cana-3280 61 25 . . PUNCT cana-3280 62 1 6s 6s NUM cana-3280 62 2 ( ( PUNCT cana-3280 62 3 2025 2025 NUM cana-3280 62 4 ) ) PUNCT cana-3280 62 5 125 125 NUM cana-3280 62 6 https://internationalpubls.com https://internationalpubls.com NUM cana-3280 62 7 greatest great ADJ cana-3280 62 8 performance performance NOUN cana-3280 62 9 , , PUNCT cana-3280 62 10 and and CCONJ cana-3280 62 11 the the DET cana-3280 62 12 highest high ADJ cana-3280 62 13 accuracy accuracy NOUN cana-3280 62 14 , , PUNCT cana-3280 62 15 with with ADP cana-3280 62 16 the the DET cana-3280 62 17 least least ADJ cana-3280 62 18 loss loss NOUN cana-3280 62 19 , , PUNCT cana-3280 62 20 as as SCONJ cana-3280 62 21 shown show VERB cana-3280 62 22 by by ADP cana-3280 62 23 the the DET cana-3280 62 24 results result NOUN cana-3280 62 25 of of ADP cana-3280 62 26 experiments experiment NOUN cana-3280 62 27 [ [ X cana-3280 62 28 19 19 NUM cana-3280 62 29 ] ] PUNCT cana-3280 62 30 . . PUNCT cana-3280 63 1 sarveshwaran sarveshwaran ADJ cana-3280 63 2 2022 2022 NUM cana-3280 63 3 et et PROPN cana-3280 63 4 al al PROPN cana-3280 63 5 . . PUNCT cana-3280 64 1 a a DET cana-3280 64 2 person person NOUN cana-3280 64 3 's 's PART cana-3280 64 4 action action NOUN cana-3280 64 5 is be AUX cana-3280 64 6 intended intend VERB cana-3280 64 7 to to PART cana-3280 64 8 be be AUX cana-3280 64 9 recognized recognize VERB cana-3280 64 10 by by ADP cana-3280 64 11 human human ADJ cana-3280 64 12 activity activity NOUN cana-3280 64 13 recognition recognition NOUN cana-3280 64 14 ( ( PUNCT cana-3280 64 15 har har NOUN cana-3280 64 16 ) ) PUNCT cana-3280 64 17 , , PUNCT cana-3280 64 18 which which PRON cana-3280 64 19 is be AUX cana-3280 64 20 based base VERB cana-3280 64 21 on on ADP cana-3280 64 22 a a DET cana-3280 64 23 set set NOUN cana-3280 64 24 of of ADP cana-3280 64 25 sensor sensor NOUN cana-3280 64 26 measurements measurement NOUN cana-3280 64 27 . . PUNCT cana-3280 65 1 economic economic ADJ cana-3280 65 2 and and CCONJ cana-3280 65 3 noneconomic noneconomic ADJ cana-3280 65 4 elements element NOUN cana-3280 65 5 can can AUX cana-3280 65 6 both both PRON cana-3280 65 7 be be AUX cana-3280 65 8 used use VERB cana-3280 65 9 to to PART cana-3280 65 10 classify classify VERB cana-3280 65 11 human human ADJ cana-3280 65 12 activity activity NOUN cana-3280 65 13 recognition recognition NOUN cana-3280 65 14 . . PUNCT cana-3280 66 1 the the DET cana-3280 66 2 economic economic ADJ cana-3280 66 3 kind kind NOUN cana-3280 66 4 is be AUX cana-3280 66 5 employed employ VERB cana-3280 66 6 to to PART cana-3280 66 7 bring bring VERB cana-3280 66 8 in in ADP cana-3280 66 9 money money NOUN cana-3280 66 10 . . PUNCT cana-3280 67 1 the the DET cana-3280 67 2 non non ADJ cana-3280 67 3 - - ADJ cana-3280 67 4 economic economic ADJ cana-3280 67 5 type type NOUN cana-3280 67 6 is be AUX cana-3280 67 7 utilized utilize VERB cana-3280 67 8 to to PART cana-3280 67 9 promote promote VERB cana-3280 67 10 mental mental ADJ cana-3280 67 11 well well ADV cana-3280 67 12 - - PUNCT cana-3280 67 13 being being NOUN cana-3280 67 14 . . PUNCT cana-3280 68 1 applications application NOUN cana-3280 68 2 for for ADP cana-3280 68 3 identifying identify VERB cana-3280 68 4 human human ADJ cana-3280 68 5 activity activity NOUN cana-3280 68 6 include include VERB cana-3280 68 7 but but CCONJ cana-3280 68 8 are be AUX cana-3280 68 9 not not PART cana-3280 68 10 limited limit VERB cana-3280 68 11 to to ADP cana-3280 68 12 , , PUNCT cana-3280 68 13 smart smart ADJ cana-3280 68 14 homes home NOUN cana-3280 68 15 , , PUNCT cana-3280 68 16 smart smart ADJ cana-3280 68 17 traffic traffic NOUN cana-3280 68 18 , , PUNCT cana-3280 68 19 aged aged ADJ cana-3280 68 20 care care NOUN cana-3280 68 21 , , PUNCT cana-3280 68 22 thief thief NOUN cana-3280 68 23 detection detection NOUN cana-3280 68 24 in in ADP cana-3280 68 25 public public ADJ cana-3280 68 26 spaces space NOUN cana-3280 68 27 , , PUNCT cana-3280 68 28 convalescence convalescence NOUN cana-3280 68 29 , , PUNCT cana-3280 68 30 and and CCONJ cana-3280 68 31 people people NOUN cana-3280 68 32 work work VERB cana-3280 68 33 assessments assessment NOUN cana-3280 68 34 . . PUNCT cana-3280 69 1 this this DET cana-3280 69 2 study study NOUN cana-3280 69 3 discusses discuss VERB cana-3280 69 4 the the DET cana-3280 69 5 deep deep ADJ cana-3280 69 6 learning learning NOUN cana-3280 69 7 model model NOUN cana-3280 69 8 , , PUNCT cana-3280 69 9 benefits benefit NOUN cana-3280 69 10 , , PUNCT cana-3280 69 11 and and CCONJ cana-3280 69 12 dataset dataset NOUN cana-3280 69 13 utilized utilize VERB cana-3280 69 14 for for ADP cana-3280 69 15 recognizing recognize VERB cana-3280 69 16 human human ADJ cana-3280 69 17 behaviour behaviour NOUN cana-3280 69 18 . . PUNCT cana-3280 70 1 using use VERB cana-3280 70 2 deep deep ADV cana-3280 70 3 supervised supervised ADJ cana-3280 70 4 and and CCONJ cana-3280 70 5 deep deep ADJ cana-3280 70 6 unsupervised unsupervised ADJ cana-3280 70 7 learning learning NOUN cana-3280 70 8 models model NOUN cana-3280 70 9 , , PUNCT cana-3280 70 10 the the DET cana-3280 70 11 main main ADJ cana-3280 70 12 har har NOUN cana-3280 70 13 issues issue NOUN cana-3280 70 14 have have AUX cana-3280 70 15 been be AUX cana-3280 70 16 gathered gather VERB cana-3280 70 17 [ [ PUNCT cana-3280 70 18 20 20 NUM cana-3280 70 19 ] ] PUNCT cana-3280 70 20 . . PUNCT cana-3280 71 1 kumar kumar PROPN cana-3280 71 2 2022 2022 PROPN cana-3280 71 3 et et PROPN cana-3280 71 4 al al PROPN cana-3280 71 5 . . PUNCT cana-3280 72 1 the the DET cana-3280 72 2 majority majority NOUN cana-3280 72 3 of of ADP cana-3280 72 4 learning learn VERB cana-3280 72 5 algorithms algorithm NOUN cana-3280 72 6 that that PRON cana-3280 72 7 have have AUX cana-3280 72 8 been be AUX cana-3280 72 9 proposed propose VERB cana-3280 72 10 have have AUX cana-3280 72 11 compared compare VERB cana-3280 72 12 activity activity NOUN cana-3280 72 13 data datum NOUN cana-3280 72 14 to to ADP cana-3280 72 15 the the DET cana-3280 72 16 fuel fuel NOUN cana-3280 72 17 used use VERB cana-3280 72 18 in in ADP cana-3280 72 19 automobiles automobile NOUN cana-3280 72 20 . . PUNCT cana-3280 73 1 having have VERB cana-3280 73 2 enough enough ADJ cana-3280 73 3 activity activity NOUN cana-3280 73 4 data datum NOUN cana-3280 73 5 at at ADP cana-3280 73 6 hand hand NOUN cana-3280 73 7 can can AUX cana-3280 73 8 increase increase VERB cana-3280 73 9 the the DET cana-3280 73 10 ability ability NOUN cana-3280 73 11 of of ADP cana-3280 73 12 learning learn VERB cana-3280 73 13 algorithms algorithm NOUN cana-3280 73 14 to to PART cana-3280 73 15 recognize recognize VERB cana-3280 73 16 activity activity NOUN cana-3280 73 17 patterns pattern NOUN cana-3280 73 18 . . PUNCT cana-3280 74 1 this this DET cana-3280 74 2 study study NOUN cana-3280 74 3 contains contain VERB cana-3280 74 4 data datum NOUN cana-3280 74 5 from from ADP cana-3280 74 6 smartphone smartphone NOUN cana-3280 74 7 sensors sensor NOUN cana-3280 74 8 ( ( PUNCT cana-3280 74 9 accelerometer accelerometer NOUN cana-3280 74 10 & & CCONJ cana-3280 74 11 gyroscope gyroscope PROPN cana-3280 74 12 ) ) PUNCT cana-3280 74 13 worn wear VERB cana-3280 74 14 around around ADP cana-3280 74 15 the the DET cana-3280 74 16 subjects subject NOUN cana-3280 74 17 ' ' PART cana-3280 74 18 waists waist NOUN cana-3280 74 19 during during ADP cana-3280 74 20 the the DET cana-3280 74 21 activities activity NOUN cana-3280 74 22 , , PUNCT cana-3280 74 23 as as ADV cana-3280 74 24 well well ADV cana-3280 74 25 as as ADP cana-3280 74 26 the the DET cana-3280 74 27 flaap flaap PROPN cana-3280 74 28 ( ( PUNCT cana-3280 74 29 finding find VERB cana-3280 74 30 and and CCONJ cana-3280 74 31 learning learn VERB cana-3280 74 32 the the DET cana-3280 74 33 associated associated ADJ cana-3280 74 34 activity activity NOUN cana-3280 74 35 patterns pattern NOUN cana-3280 74 36 ) ) PUNCT cana-3280 74 37 activity activity NOUN cana-3280 74 38 dataset dataset NOUN cana-3280 74 39 . . PUNCT cana-3280 75 1 in in ADP cana-3280 75 2 this this DET cana-3280 75 3 dataset dataset NOUN cana-3280 75 4 , , PUNCT cana-3280 75 5 ten ten NUM cana-3280 75 6 behaviours behaviour NOUN cana-3280 75 7 were be AUX cana-3280 75 8 noted note VERB cana-3280 75 9 by by ADP cana-3280 75 10 eight eight NUM cana-3280 75 11 distinct distinct ADJ cana-3280 75 12 participants participant NOUN cana-3280 75 13 . . PUNCT cana-3280 76 1 a a DET cana-3280 76 2 steady steady ADJ cana-3280 76 3 100hz 100hz ADJ cana-3280 76 4 sampling sample VERB cana-3280 76 5 rate rate NOUN cana-3280 76 6 was be AUX cana-3280 76 7 used use VERB cana-3280 76 8 to to PART cana-3280 76 9 gather gather VERB cana-3280 76 10 millions million NOUN cana-3280 76 11 of of ADP cana-3280 76 12 samples sample NOUN cana-3280 76 13 of of ADP cana-3280 76 14 raw raw ADJ cana-3280 76 15 sensor sensor NOUN cana-3280 76 16 activity activity NOUN cana-3280 76 17 data datum NOUN cana-3280 76 18 between between ADP cana-3280 76 19 february february PROPN cana-3280 76 20 1 1 NUM cana-3280 76 21 and and CCONJ cana-3280 76 22 may may AUX cana-3280 76 23 31 31 NUM cana-3280 76 24 of of ADP cana-3280 76 25 2022 2022 NUM cana-3280 76 26 . . PUNCT cana-3280 77 1 it it PRON cana-3280 77 2 was be AUX cana-3280 77 3 difficult difficult ADJ cana-3280 77 4 to to PART cana-3280 77 5 identify identify VERB cana-3280 77 6 the the DET cana-3280 77 7 activities activity NOUN cana-3280 77 8 for for ADP cana-3280 77 9 daily daily ADJ cana-3280 77 10 living living NOUN cana-3280 77 11 ( ( PUNCT cana-3280 77 12 adl adl PROPN cana-3280 77 13 ) ) PUNCT cana-3280 77 14 using use VERB cana-3280 77 15 the the DET cana-3280 77 16 har har NOUN cana-3280 77 17 ( ( PUNCT cana-3280 77 18 human human ADJ cana-3280 77 19 activity activity NOUN cana-3280 77 20 recognition recognition NOUN cana-3280 77 21 ) ) PUNCT cana-3280 77 22 data datum NOUN cana-3280 77 23 sets set NOUN cana-3280 77 24 , , PUNCT cana-3280 77 25 which which PRON cana-3280 77 26 keep keep VERB cana-3280 77 27 track track NOUN cana-3280 77 28 of of ADP cana-3280 77 29 these these DET cana-3280 77 30 behaviours behaviour NOUN cana-3280 77 31 and and CCONJ cana-3280 77 32 discover discover VERB cana-3280 77 33 relationships relationship NOUN cana-3280 77 34 between between ADP cana-3280 77 35 activity activity NOUN cana-3280 77 36 patterns pattern NOUN cana-3280 77 37 . . PUNCT cana-3280 78 1 the the DET cana-3280 78 2 flaap flaap PROPN cana-3280 78 3 dataset dataset VERB cana-3280 78 4 closes close VERB cana-3280 78 5 this this DET cana-3280 78 6 gap gap NOUN cana-3280 78 7 and and CCONJ cana-3280 78 8 can can AUX cana-3280 78 9 be be AUX cana-3280 78 10 used use VERB cana-3280 78 11 to to PART cana-3280 78 12 identify identify VERB cana-3280 78 13 the the DET cana-3280 78 14 associated associated ADJ cana-3280 78 15 adl adl PROPN cana-3280 78 16 patterns pattern NOUN cana-3280 78 17 . . PUNCT cana-3280 79 1 the the DET cana-3280 79 2 results result NOUN cana-3280 79 3 of of ADP cana-3280 79 4 the the DET cana-3280 79 5 experiment experiment NOUN cana-3280 79 6 show show VERB cana-3280 79 7 that that SCONJ cana-3280 79 8 the the DET cana-3280 79 9 learning learning NOUN cana-3280 79 10 technique technique NOUN cana-3280 79 11 random random ADJ cana-3280 79 12 forest forest NOUN cana-3280 79 13 ( ( PUNCT cana-3280 79 14 rf rf NOUN cana-3280 79 15 ) ) PUNCT cana-3280 79 16 , , PUNCT cana-3280 79 17 which which PRON cana-3280 79 18 was be AUX cana-3280 79 19 applied apply VERB cana-3280 79 20 , , PUNCT cana-3280 79 21 correctly correctly ADV cana-3280 79 22 detected detect VERB cana-3280 79 23 the the DET cana-3280 79 24 activities activity NOUN cana-3280 79 25 with with ADP cana-3280 79 26 a a DET cana-3280 79 27 degree degree NOUN cana-3280 79 28 of of ADP cana-3280 79 29 accuracy accuracy NOUN cana-3280 79 30 of of ADP cana-3280 79 31 about about ADV cana-3280 79 32 77.22 77.22 NUM cana-3280 79 33 % % NOUN cana-3280 79 34 . . PUNCT cana-3280 80 1 by by ADP cana-3280 80 2 creating create VERB cana-3280 80 3 more more ADJ cana-3280 80 4 delicate delicate ADJ cana-3280 80 5 learning learning NOUN cana-3280 80 6 models model NOUN cana-3280 80 7 that that PRON cana-3280 80 8 will will AUX cana-3280 80 9 improve improve VERB cana-3280 80 10 recognition recognition NOUN cana-3280 80 11 rates rate NOUN cana-3280 80 12 , , PUNCT cana-3280 80 13 researchers researcher NOUN cana-3280 80 14 can can AUX cana-3280 80 15 fill fill VERB cana-3280 80 16 a a DET cana-3280 80 17 research research NOUN cana-3280 80 18 need need NOUN cana-3280 80 19 in in ADP cana-3280 80 20 this this DET cana-3280 80 21 area area NOUN cana-3280 80 22 by by ADP cana-3280 80 23 utilizing utilize VERB cana-3280 80 24 the the DET cana-3280 80 25 flaap flaap PROPN cana-3280 80 26 dataset dataset PROPN cana-3280 80 27 's 's PART cana-3280 80 28 applied apply VERB cana-3280 80 29 rf rf NOUN cana-3280 80 30 learning learn VERB cana-3280 80 31 technique technique NOUN cana-3280 80 32 . . PUNCT cana-3280 81 1 the the DET cana-3280 81 2 scientific scientific ADJ cana-3280 81 3 community community NOUN cana-3280 81 4 may may AUX cana-3280 81 5 also also ADV cana-3280 81 6 take take VERB cana-3280 81 7 a a DET cana-3280 81 8 keen keen ADJ cana-3280 81 9 interest interest NOUN cana-3280 81 10 in in ADP cana-3280 81 11 studying study VERB cana-3280 81 12 how how SCONJ cana-3280 81 13 algorithms algorithm NOUN cana-3280 81 14 pick pick VERB cana-3280 81 15 up up ADP cana-3280 81 16 new new ADJ cana-3280 81 17 information information NOUN cana-3280 81 18 when when SCONJ cana-3280 81 19 employing employ VERB cana-3280 81 20 different different ADJ cana-3280 81 21 data datum NOUN cana-3280 81 22 pre pre ADJ cana-3280 81 23 - - ADJ cana-3280 81 24 processing processing ADJ cana-3280 81 25 approaches approach NOUN cana-3280 81 26 , , PUNCT cana-3280 81 27 as as ADV cana-3280 81 28 well well ADV cana-3280 81 29 as as ADP cana-3280 81 30 knowledge knowledge NOUN cana-3280 81 31 transfer transfer NOUN cana-3280 81 32 to to ADP cana-3280 81 33 target target VERB cana-3280 81 34 domains domain NOUN cana-3280 81 35 , , PUNCT cana-3280 81 36 and and CCONJ cana-3280 81 37 further further ADJ cana-3280 81 38 methods method NOUN cana-3280 81 39 [ [ X cana-3280 81 40 21 21 NUM cana-3280 81 41 ] ] PUNCT cana-3280 81 42 . . PUNCT cana-3280 82 1 3 3 X cana-3280 82 2 . . NUM cana-3280 82 3 proposed propose VERB cana-3280 82 4 methodology methodology NOUN cana-3280 82 5 in in ADP cana-3280 82 6 this this DET cana-3280 82 7 discussion discussion NOUN cana-3280 82 8 , , PUNCT cana-3280 82 9 the the DET cana-3280 82 10 methodology methodology NOUN cana-3280 82 11 for for ADP cana-3280 82 12 human human ADJ cana-3280 82 13 activity activity NOUN cana-3280 82 14 recognition recognition NOUN cana-3280 82 15 is be AUX cana-3280 82 16 discussed discuss VERB cana-3280 82 17 . . PUNCT cana-3280 83 1 first first ADV cana-3280 83 2 , , PUNCT cana-3280 83 3 data datum NOUN cana-3280 83 4 from from ADP cana-3280 83 5 the the DET cana-3280 83 6 ufc ufc NOUN cana-3280 83 7 50 50 NUM cana-3280 83 8 is be AUX cana-3280 83 9 collected collect VERB cana-3280 83 10 , , PUNCT cana-3280 83 11 after after ADP cana-3280 83 12 which which PRON cana-3280 83 13 7 7 NUM cana-3280 83 14 classes class NOUN cana-3280 83 15 are be AUX cana-3280 83 16 chosen choose VERB cana-3280 83 17 . . PUNCT cana-3280 84 1 next next ADV cana-3280 84 2 , , PUNCT cana-3280 84 3 the the DET cana-3280 84 4 data data NOUN cana-3280 84 5 is be AUX cana-3280 84 6 pre pre ADJ cana-3280 84 7 - - VERB cana-3280 84 8 processed process VERB cana-3280 84 9 with with ADP cana-3280 84 10 frame frame NOUN cana-3280 84 11 capture capture NOUN cana-3280 84 12 , , PUNCT cana-3280 84 13 data datum NOUN cana-3280 84 14 resizing resizing NOUN cana-3280 84 15 , , PUNCT cana-3280 84 16 and and CCONJ cana-3280 84 17 data datum NOUN cana-3280 84 18 scaling scaling NOUN cana-3280 84 19 . . PUNCT cana-3280 85 1 finally finally ADV cana-3280 85 2 , , PUNCT cana-3280 85 3 one one NUM cana-3280 85 4 hot hot ADJ cana-3280 85 5 encoding encoding NOUN cana-3280 85 6 is be AUX cana-3280 85 7 performed perform VERB cana-3280 85 8 , , PUNCT cana-3280 85 9 followed follow VERB cana-3280 85 10 by by ADP cana-3280 85 11 modeling modeling NOUN cana-3280 85 12 using use VERB cana-3280 85 13 2dcnn 2dcnn NUM cana-3280 85 14 , , PUNCT cana-3280 85 15 lstm lstm ADJ cana-3280 85 16 , , PUNCT cana-3280 85 17 and and CCONJ cana-3280 85 18 lrcnn lrcnn VERB cana-3280 86 1 [ [ PUNCT cana-3280 86 2 22 22 NUM cana-3280 86 3 - - SYM cana-3280 86 4 23 23 NUM cana-3280 86 5 ] ] PUNCT cana-3280 86 6 . . PUNCT cana-3280 87 1 finally finally ADV cana-3280 87 2 , , PUNCT cana-3280 87 3 the the DET cana-3280 87 4 prediction prediction NOUN cana-3280 87 5 is be AUX cana-3280 87 6 evaluated evaluate VERB cana-3280 87 7 . . PUNCT cana-3280 88 1 fig fig NOUN cana-3280 88 2 . . PUNCT cana-3280 89 1 1 1 NUM cana-3280 89 2 shows show VERB cana-3280 89 3 the the DET cana-3280 89 4 proposed propose VERB cana-3280 89 5 flowchart flowchart NOUN cana-3280 89 6 and and CCONJ cana-3280 89 7 methodology methodology NOUN cana-3280 89 8 and and CCONJ cana-3280 89 9 one one NUM cana-3280 89 10 hot hot ADJ cana-3280 89 11 encoding encoding NOUN cana-3280 89 12 is be AUX cana-3280 89 13 shown show VERB cana-3280 89 14 in in ADP cana-3280 89 15 equation equation NOUN cana-3280 89 16 1 1 NUM cana-3280 89 17 . . PUNCT cana-3280 89 18 βˆ‘ βˆ‘ PUNCT cana-3280 89 19 ( ( PUNCT cana-3280 89 20 𝒴𝑖𝑀 𝒴𝑖𝑀 VERB cana-3280 89 21 𝑖=1 𝑖=1 PUNCT cana-3280 89 22 βˆ’ βˆ’ NOUN cana-3280 89 23 βˆ‘ βˆ‘ PUNCT cana-3280 89 24 𝑀𝑗 𝑀𝑗 ADP cana-3280 89 25 βˆ— βˆ— NOUN cana-3280 89 26 π‘₯𝑖𝑗)2 π‘₯𝑖𝑗)2 PROPN cana-3280 89 27 + + PUNCT cana-3280 89 28 πœ† πœ† ADP cana-3280 89 29 βˆ‘ βˆ‘ PROPN cana-3280 89 30 𝒲𝑗 𝒲𝑗 PROPN cana-3280 89 31 2𝑝 2𝑝 NOUN cana-3280 89 32 𝑗=0 𝑗=0 PROPN cana-3280 90 1 𝑝 𝑝 PROPN cana-3280 90 2 𝑗=0 𝑗=0 PROPN cana-3280 90 3 ( ( PUNCT cana-3280 90 4 1 1 X cana-3280 90 5 ) ) PUNCT cana-3280 90 6 communications communication NOUN cana-3280 90 7 on on ADP cana-3280 90 8 applied apply VERB cana-3280 90 9 nonlinear nonlinear ADJ cana-3280 90 10 analysis analysis NOUN cana-3280 90 11 issn issn NOUN cana-3280 90 12 : : PUNCT cana-3280 90 13 1074 1074 NUM cana-3280 90 14 - - PUNCT cana-3280 90 15 133x 133x NUM cana-3280 90 16 vol vol NOUN cana-3280 90 17 32 32 NUM cana-3280 90 18 no no NOUN cana-3280 90 19 . . PUNCT cana-3280 91 1 6s 6s NUM cana-3280 91 2 ( ( PUNCT cana-3280 91 3 2025 2025 NUM cana-3280 91 4 ) ) PUNCT cana-3280 91 5 126 126 NUM cana-3280 91 6 https://internationalpubls.com https://internationalpubls.com X cana-3280 91 7 figure figure NOUN cana-3280 91 8 1 1 NUM cana-3280 91 9 : : PUNCT cana-3280 91 10 proposed propose VERB cana-3280 91 11 flowchart flowchart PROPN cana-3280 91 12 a. a. PROPN cana-3280 91 13 data data PROPN cana-3280 91 14 collection collection NOUN cana-3280 91 15 the the DET cana-3280 91 16 website website NOUN cana-3280 91 17 at at ADP cana-3280 91 18 [ [ X cana-3280 91 19 24 24 NUM cana-3280 91 20 ] ] PUNCT cana-3280 91 21 https://www.crcv.ucf.edu/data/ucf50.php https://www.crcv.ucf.edu/data/ucf50.php PROPN cana-3280 91 22 was be AUX cana-3280 91 23 used use VERB cana-3280 91 24 to to PART cana-3280 91 25 gather gather VERB cana-3280 91 26 the the DET cana-3280 91 27 information information NOUN cana-3280 91 28 . . PUNCT cana-3280 92 1 ucf50 ucf50 PROPN cana-3280 92 2 is be AUX cana-3280 92 3 a a DET cana-3280 92 4 set set NOUN cana-3280 92 5 of of ADP cana-3280 92 6 action action NOUN cana-3280 92 7 recognition recognition NOUN cana-3280 92 8 data datum NOUN cana-3280 92 9 that that PRON cana-3280 92 10 includes include VERB cana-3280 92 11 50 50 NUM cana-3280 92 12 action action NOUN cana-3280 92 13 categories category NOUN cana-3280 92 14 that that PRON cana-3280 92 15 were be AUX cana-3280 92 16 taken take VERB cana-3280 92 17 directly directly ADV cana-3280 92 18 from from ADP cana-3280 92 19 real real ADJ cana-3280 92 20 youtube youtube NOUN cana-3280 92 21 videos video NOUN cana-3280 92 22 . . PUNCT cana-3280 93 1 this this DET cana-3280 93 2 data datum NOUN cana-3280 93 3 set set NOUN cana-3280 93 4 's 's PART cana-3280 93 5 development development NOUN cana-3280 93 6 , , PUNCT cana-3280 93 7 the the DET cana-3280 93 8 youtube youtube NOUN cana-3280 93 9 action action NOUN cana-3280 93 10 information information NOUN cana-3280 93 11 set set NOUN cana-3280 93 12 ( ( PUNCT cana-3280 93 13 ucf11 ucf11 NOUN cana-3280 93 14 ) ) PUNCT cana-3280 93 15 , , PUNCT cana-3280 93 16 comprises comprise VERB cana-3280 93 17 eleven eleven NUM cana-3280 93 18 distinct distinct ADJ cana-3280 93 19 action action NOUN cana-3280 93 20 types type NOUN cana-3280 93 21 . . PUNCT cana-3280 94 1 the the DET cana-3280 94 2 ucf50 ucf50 PROPN cana-3280 94 3 data datum NOUN cana-3280 94 4 collection collection NOUN cana-3280 94 5 on on ADP cana-3280 94 6 50 50 NUM cana-3280 94 7 activity activity NOUN cana-3280 94 8 categories category NOUN cana-3280 94 9 on on ADP cana-3280 94 10 youtube youtube NOUN cana-3280 94 11 includes include VERB cana-3280 94 12 : : PUNCT cana-3280 94 13 some some DET cana-3280 94 14 examples example NOUN cana-3280 94 15 of of ADP cana-3280 94 16 athletics athletic NOUN cana-3280 94 17 include include VERB cana-3280 94 18 bench bench NOUN cana-3280 94 19 pressing press VERB cana-3280 94 20 , , PUNCT cana-3280 94 21 biking biking NOUN cana-3280 94 22 , , PUNCT cana-3280 94 23 shooting shoot VERB cana-3280 94 24 baskets basket NOUN cana-3280 94 25 , , PUNCT cana-3280 94 26 swimming swim VERB cana-3280 94 27 the the DET cana-3280 94 28 breaststroke breaststroke NOUN cana-3280 94 29 , , PUNCT cana-3280 94 30 and and CCONJ cana-3280 94 31 biking biking NOUN cana-3280 94 32 . . PUNCT cana-3280 95 1 diverting divert VERB cana-3280 95 2 , , PUNCT cana-3280 95 3 drumming drumming NOUN cana-3280 95 4 , , PUNCT cana-3280 95 5 fencing fencing NOUN cana-3280 95 6 , , PUNCT cana-3280 95 7 golfing golfing NOUN cana-3280 95 8 , , PUNCT cana-3280 95 9 playing play VERB cana-3280 95 10 the the DET cana-3280 95 11 guitar guitar NOUN cana-3280 95 12 , , PUNCT cana-3280 95 13 high high ADJ cana-3280 95 14 jumping jumping NOUN cana-3280 95 15 , , PUNCT cana-3280 95 16 horse horse NOUN cana-3280 95 17 racing racing NOUN cana-3280 95 18 , , PUNCT cana-3280 95 19 riding ride VERB cana-3280 95 20 a a DET cana-3280 95 21 horse horse NOUN cana-3280 95 22 , , PUNCT cana-3280 95 23 juggling juggling NOUN cana-3280 95 24 balls ball NOUN cana-3280 95 25 , , PUNCT cana-3280 95 26 jumping jump VERB cana-3280 95 27 rope rope NOUN cana-3280 95 28 , , PUNCT cana-3280 95 29 jumping jumping NOUN cana-3280 95 30 jacks jack NOUN cana-3280 95 31 , , PUNCT cana-3280 95 32 kayaking kayaking NOUN cana-3280 95 33 , , PUNCT cana-3280 95 34 lunges lunge NOUN cana-3280 95 35 , , PUNCT cana-3280 95 36 participating participate VERB cana-3280 95 37 in in ADP cana-3280 95 38 a a DET cana-3280 95 39 military military ADJ cana-3280 95 40 parade parade NOUN cana-3280 95 41 , , PUNCT cana-3280 95 42 mixing mix VERB cana-3280 95 43 batter batter NOUN cana-3280 95 44 , , PUNCT cana-3280 95 45 nun nun NOUN cana-3280 95 46 chucks chuck NOUN cana-3280 95 47 , , PUNCT cana-3280 95 48 playing play VERB cana-3280 95 49 the the DET cana-3280 95 50 piano piano NOUN cana-3280 95 51 , , PUNCT cana-3280 95 52 tossing toss VERB cana-3280 95 53 pizza pizza NOUN cana-3280 95 54 , , PUNCT cana-3280 95 55 pole pole NOUN cana-3280 95 56 vaulting vaulting NOUN cana-3280 95 57 , , PUNCT cana-3280 95 58 as as ADV cana-3280 95 59 well well ADV cana-3280 95 60 as as ADP cana-3280 95 61 pommel pommel NOUN cana-3280 95 62 horse horse NOUN cana-3280 95 63 are be AUX cana-3280 95 64 a a DET cana-3280 95 65 few few ADJ cana-3280 95 66 of of ADP cana-3280 95 67 the the DET cana-3280 95 68 additional additional ADJ cana-3280 95 69 activities activity NOUN cana-3280 95 70 . . PUNCT cana-3280 96 1 punches punch NOUN cana-3280 96 2 , , PUNCT cana-3280 96 3 pullups pullup NOUN cana-3280 96 4 , , PUNCT cana-3280 96 5 and and CCONJ cana-3280 96 6 pushups pushup NOUN cana-3280 96 7 you you PRON cana-3280 96 8 can can AUX cana-3280 96 9 do do VERB cana-3280 96 10 sports sport NOUN cana-3280 96 11 like like ADP cana-3280 96 12 rowing rowing NOUN cana-3280 96 13 , , PUNCT cana-3280 96 14 salsa salsa NOUN cana-3280 96 15 spinning spinning NOUN cana-3280 96 16 , , PUNCT cana-3280 96 17 skateboarding skateboarding ADJ cana-3280 96 18 , , PUNCT cana-3280 96 19 skiing skiing NOUN cana-3280 96 20 , , PUNCT cana-3280 96 21 ski ski NOUN cana-3280 96 22 jet jet NOUN cana-3280 96 23 , , PUNCT cana-3280 96 24 soccer soccer NOUN cana-3280 96 25 juggling juggling NOUN cana-3280 96 26 , , PUNCT cana-3280 96 27 swinging swinge VERB cana-3280 96 28 , , PUNCT cana-3280 96 29 playing play VERB cana-3280 96 30 the the DET cana-3280 96 31 table table NOUN cana-3280 96 32 , , PUNCT cana-3280 96 33 tai tai PROPN cana-3280 96 34 chi chi PROPN cana-3280 96 35 , , PUNCT cana-3280 96 36 tennis tennis NOUN cana-3280 96 37 swinging swinging NOUN cana-3280 96 38 , , PUNCT cana-3280 96 39 trampolining trampoline VERB cana-3280 96 40 , , PUNCT cana-3280 96 41 playing play VERB cana-3280 96 42 the the DET cana-3280 96 43 violin violin NOUN cana-3280 96 44 , , PUNCT cana-3280 96 45 volleyball volleyball NOUN cana-3280 96 46 spiking spiking NOUN cana-3280 96 47 , , PUNCT cana-3280 96 48 walks walk VERB cana-3280 96 49 with with ADP cana-3280 96 50 a a DET cana-3280 96 51 dog dog NOUN cana-3280 96 52 and and CCONJ cana-3280 96 53 yo yo PROPN cana-3280 96 54 - - PUNCT cana-3280 96 55 yo yo PROPN cana-3280 96 56 . . PUNCT cana-3280 96 57 b. b. PROPN cana-3280 97 1 pre pre ADJ cana-3280 97 2 - - ADJ cana-3280 97 3 processing processing NOUN cana-3280 97 4 get get VERB cana-3280 97 5 all all DET cana-3280 97 6 the the DET cana-3280 97 7 videos video NOUN cana-3280 97 8 , , PUNCT cana-3280 97 9 capture capture NOUN cana-3280 97 10 frames frame NOUN cana-3280 97 11 from from ADP cana-3280 97 12 the the DET cana-3280 97 13 videos video NOUN cana-3280 97 14 , , PUNCT cana-3280 97 15 overlay overlay NOUN cana-3280 97 16 text text NOUN cana-3280 97 17 , , PUNCT cana-3280 97 18 resize resize VERB cana-3280 97 19 frames frame NOUN cana-3280 97 20 with with ADP cana-3280 97 21 64 64 NUM cana-3280 97 22 heights height NOUN cana-3280 97 23 as as ADV cana-3280 97 24 well well ADV cana-3280 97 25 as as ADP cana-3280 97 26 64 64 NUM cana-3280 97 27 widths width NOUN cana-3280 97 28 , , PUNCT cana-3280 97 29 consider consider VERB cana-3280 97 30 the the DET cana-3280 97 31 length length NOUN cana-3280 97 32 of of ADP cana-3280 97 33 the the DET cana-3280 97 34 sequence sequence NOUN cana-3280 97 35 at at ADP cana-3280 97 36 20 20 NUM cana-3280 97 37 , , PUNCT cana-3280 97 38 get get VERB cana-3280 97 39 all all DET cana-3280 97 40 classes class NOUN cana-3280 97 41 with with ADP cana-3280 97 42 a a DET cana-3280 97 43 list list NOUN cana-3280 97 44 , , PUNCT cana-3280 97 45 extract extract VERB cana-3280 97 46 frames frame NOUN cana-3280 97 47 from from ADP cana-3280 97 48 the the DET cana-3280 97 49 data datum NOUN cana-3280 97 50 , , PUNCT cana-3280 97 51 and and CCONJ cana-3280 97 52 normalize normalize VERB cana-3280 97 53 all all DET cana-3280 97 54 frames frame NOUN cana-3280 97 55 via via ADP cana-3280 97 56 pixel pixel PROPN cana-3280 97 57 division division NOUN cana-3280 97 58 1255 1255 NUM cana-3280 97 59 . . PUNCT cana-3280 98 1 finally finally ADV cana-3280 98 2 , , PUNCT cana-3280 98 3 convert convert VERB cana-3280 98 4 all all DET cana-3280 98 5 numerically numerically ADV cana-3280 98 6 categorical categorical ADJ cana-3280 98 7 data datum NOUN cana-3280 98 8 to to ADP cana-3280 98 9 one one NUM cana-3280 98 10 hot hot ADJ cana-3280 98 11 encoding encoding NOUN cana-3280 98 12 . . PUNCT cana-3280 99 1 c. c. PROPN cana-3280 99 2 data data PROPN cana-3280 99 3 splitting splitting NOUN cana-3280 99 4 data datum NOUN cana-3280 99 5 have have AUX cana-3280 99 6 been be AUX cana-3280 99 7 divided divide VERB cana-3280 99 8 80:20 80:20 NUM cana-3280 99 9 . . PUNCT cana-3280 100 1 training training NOUN cana-3280 100 2 takes take VERB cana-3280 100 3 up up ADP cana-3280 100 4 80 80 NUM cana-3280 100 5 % % NOUN cana-3280 100 6 of of ADP cana-3280 100 7 the the DET cana-3280 100 8 time time NOUN cana-3280 100 9 , , PUNCT cana-3280 100 10 whereas whereas SCONJ cana-3280 100 11 testing test VERB cana-3280 100 12 only only ADV cana-3280 100 13 occupies occupy VERB cana-3280 100 14 20 20 NUM cana-3280 100 15 % % NOUN cana-3280 100 16 . . PUNCT cana-3280 101 1 using use VERB cana-3280 101 2 the the DET cana-3280 101 3 machine machine NOUN cana-3280 101 4 learning learning NOUN cana-3280 101 5 ( ( PUNCT cana-3280 101 6 ml ml NOUN cana-3280 101 7 ) ) PUNCT cana-3280 101 8 data datum NOUN cana-3280 101 9 splitting splitting NOUN cana-3280 101 10 technique technique NOUN cana-3280 101 11 , , PUNCT cana-3280 101 12 overfitting overfitte VERB cana-3280 101 13 can can AUX cana-3280 101 14 be be AUX cana-3280 101 15 prevented prevent VERB cana-3280 101 16 . . PUNCT cana-3280 102 1 when when SCONJ cana-3280 102 2 a a DET cana-3280 102 3 machine machine NOUN cana-3280 102 4 learning learn VERB cana-3280 102 5 system system NOUN cana-3280 102 6 can can AUX cana-3280 102 7 accurately accurately ADV cana-3280 102 8 fit fit VERB cana-3280 102 9 its its PRON cana-3280 102 10 training training NOUN cana-3280 102 11 data datum NOUN cana-3280 102 12 but but CCONJ cana-3280 102 13 not not PART cana-3280 102 14 any any DET cana-3280 102 15 new new ADJ cana-3280 102 16 data datum NOUN cana-3280 102 17 , , PUNCT cana-3280 102 18 this this PRON cana-3280 102 19 is be AUX cana-3280 102 20 referred refer VERB cana-3280 102 21 to to ADP cana-3280 102 22 as as ADP cana-3280 102 23 overfitting overfitte VERB cana-3280 102 24 . . PUNCT cana-3280 103 1 this this DET cana-3280 103 2 circumstance circumstance NOUN cana-3280 103 3 falls fall VERB cana-3280 103 4 within within ADP cana-3280 103 5 that that DET cana-3280 103 6 group group NOUN cana-3280 103 7 . . PUNCT cana-3280 104 1 it it PRON cana-3280 104 2 is be AUX cana-3280 104 3 typical typical ADJ cana-3280 104 4 to to PART cana-3280 104 5 divide divide VERB cana-3280 104 6 this this DET cana-3280 104 7 initial initial ADJ cana-3280 104 8 data datum NOUN cana-3280 104 9 into into ADP cana-3280 104 10 three three NUM cana-3280 104 11 communications communication NOUN cana-3280 104 12 on on ADP cana-3280 104 13 applied apply VERB cana-3280 104 14 nonlinear nonlinear ADJ cana-3280 104 15 analysis analysis NOUN cana-3280 104 16 issn issn NOUN cana-3280 104 17 : : PUNCT cana-3280 104 18 1074 1074 NUM cana-3280 104 19 - - PUNCT cana-3280 104 20 133x 133x NUM cana-3280 104 21 vol vol NOUN cana-3280 104 22 32 32 NUM cana-3280 104 23 no no NOUN cana-3280 104 24 . . PUNCT cana-3280 105 1 6s 6s NUM cana-3280 105 2 ( ( PUNCT cana-3280 105 3 2025 2025 NUM cana-3280 105 4 ) ) PUNCT cana-3280 105 5 127 127 NUM cana-3280 105 6 https://internationalpubls.com https://internationalpubls.com X cana-3280 105 7 to to ADP cana-3280 105 8 four four NUM cana-3280 105 9 different different ADJ cana-3280 105 10 subgroups subgroup NOUN cana-3280 105 11 before before ADP cana-3280 105 12 putting put VERB cana-3280 105 13 it it PRON cana-3280 105 14 into into ADP cana-3280 105 15 an an DET cana-3280 105 16 ml ml NOUN cana-3280 105 17 model model NOUN cana-3280 105 18 . . PUNCT cana-3280 106 1 the the DET cana-3280 106 2 testing testing NOUN cana-3280 106 3 and and CCONJ cana-3280 106 4 training training NOUN cana-3280 106 5 datasets dataset NOUN cana-3280 106 6 are be AUX cana-3280 106 7 examples example NOUN cana-3280 106 8 of of ADP cana-3280 106 9 common common ADJ cana-3280 106 10 datasets dataset NOUN cana-3280 106 11 . . PUNCT cana-3280 107 1 to to PART cana-3280 107 2 improve improve VERB cana-3280 107 3 the the DET cana-3280 107 4 amount amount NOUN cana-3280 107 5 of of ADP cana-3280 107 6 training training NOUN cana-3280 107 7 data datum NOUN cana-3280 107 8 for for ADP cana-3280 107 9 each each DET cana-3280 107 10 distinct distinct ADJ cana-3280 107 11 data datum NOUN cana-3280 107 12 collection collection NOUN cana-3280 107 13 , , PUNCT cana-3280 107 14 it it PRON cana-3280 107 15 is be AUX cana-3280 107 16 recommended recommend VERB cana-3280 107 17 that that SCONJ cana-3280 107 18 the the DET cana-3280 107 19 data datum NOUN cana-3280 107 20 be be AUX cana-3280 107 21 divided divide VERB cana-3280 107 22 . . PUNCT cana-3280 108 1 d. d. PROPN cana-3280 108 2 exploratory exploratory ADJ cana-3280 108 3 data datum NOUN cana-3280 108 4 analysis analysis NOUN cana-3280 108 5 ( ( PUNCT cana-3280 108 6 eda eda PROPN cana-3280 108 7 ) ) PUNCT cana-3280 108 8 exploratory exploratory ADJ cana-3280 108 9 data datum NOUN cana-3280 108 10 analysis analysis NOUN cana-3280 108 11 , , PUNCT cana-3280 108 12 sometimes sometimes ADV cana-3280 108 13 known know VERB cana-3280 108 14 by by ADP cana-3280 108 15 its its PRON cana-3280 108 16 acronym acronym NOUN cana-3280 108 17 eda eda PROPN cana-3280 108 18 , , PUNCT cana-3280 108 19 is be AUX cana-3280 108 20 an an DET cana-3280 108 21 important important ADJ cana-3280 108 22 stage stage NOUN cana-3280 108 23 in in ADP cana-3280 108 24 the the DET cana-3280 108 25 data datum NOUN cana-3280 108 26 analysis analysis NOUN cana-3280 108 27 process process NOUN cana-3280 108 28 . . PUNCT cana-3280 109 1 the the DET cana-3280 109 2 process process NOUN cana-3280 109 3 entails entail VERB cana-3280 109 4 evaluating evaluating NOUN cana-3280 109 5 , , PUNCT cana-3280 109 6 cleansing cleansing NOUN cana-3280 109 7 , , PUNCT cana-3280 109 8 and and CCONJ cana-3280 109 9 displaying display VERB cana-3280 109 10 the the DET cana-3280 109 11 data datum NOUN cana-3280 109 12 in in ADP cana-3280 109 13 order order NOUN cana-3280 109 14 to to PART cana-3280 109 15 find find VERB cana-3280 109 16 patterns pattern NOUN cana-3280 109 17 , , PUNCT cana-3280 109 18 relationships relationship NOUN cana-3280 109 19 , , PUNCT cana-3280 109 20 and and CCONJ cana-3280 109 21 insights insight NOUN cana-3280 109 22 in in ADP cana-3280 109 23 it it PRON cana-3280 109 24 . . PUNCT cana-3280 110 1 eda eda PROPN cana-3280 110 2 enables enable VERB cana-3280 110 3 analysts analyst NOUN cana-3280 110 4 and and CCONJ cana-3280 110 5 data datum NOUN cana-3280 110 6 scientists scientist NOUN cana-3280 110 7 to to PART cana-3280 110 8 gain gain VERB cana-3280 110 9 a a DET cana-3280 110 10 better well ADJ cana-3280 110 11 understanding understanding NOUN cana-3280 110 12 of of ADP cana-3280 110 13 the the DET cana-3280 110 14 primary primary ADJ cana-3280 110 15 attributes attribute NOUN cana-3280 110 16 of of ADP cana-3280 110 17 a a DET cana-3280 110 18 dataset dataset NOUN cana-3280 110 19 and and CCONJ cana-3280 110 20 to to PART cana-3280 110 21 make make VERB cana-3280 110 22 educated educated ADJ cana-3280 110 23 decisions decision NOUN cana-3280 110 24 regarding regard VERB cana-3280 110 25 whether whether SCONJ cana-3280 110 26 or or CCONJ cana-3280 110 27 not not PART cana-3280 110 28 to to PART cana-3280 110 29 proceed proceed VERB cana-3280 110 30 with with ADP cana-3280 110 31 additional additional ADJ cana-3280 110 32 analysis analysis NOUN cana-3280 110 33 or or CCONJ cana-3280 110 34 modelling modelling NOUN cana-3280 110 35 . . PUNCT cana-3280 111 1 ( ( PUNCT cana-3280 111 2 a a X cana-3280 111 3 ) ) PUNCT cana-3280 111 4 ( ( PUNCT cana-3280 111 5 b b X cana-3280 111 6 ) ) PUNCT cana-3280 111 7 figure figure NOUN cana-3280 111 8 2 2 NUM cana-3280 111 9 : : PUNCT cana-3280 111 10 count count VERB cana-3280 111 11 plot plot NOUN cana-3280 111 12 ( ( PUNCT cana-3280 111 13 a a NOUN cana-3280 111 14 ) ) PUNCT cana-3280 111 15 and and CCONJ cana-3280 111 16 histogram histogram NOUN cana-3280 111 17 ( ( PUNCT cana-3280 111 18 b b NOUN cana-3280 111 19 ) ) PUNCT cana-3280 111 20 of of ADP cana-3280 111 21 activity activity NOUN cana-3280 112 1 i i NOUN cana-3280 112 2 d d PROPN cana-3280 112 3 ( ( PUNCT cana-3280 112 4 c c NOUN cana-3280 112 5 ) ) PUNCT cana-3280 112 6 ( ( PUNCT cana-3280 112 7 d d X cana-3280 112 8 ) ) PUNCT cana-3280 112 9 figure figure NOUN cana-3280 112 10 3 3 NUM cana-3280 112 11 : : PUNCT cana-3280 112 12 violin violin NOUN cana-3280 112 13 plot plot NOUN cana-3280 112 14 ( ( PUNCT cana-3280 112 15 a a NOUN cana-3280 112 16 ) ) PUNCT cana-3280 112 17 and and CCONJ cana-3280 112 18 boxplot boxplot NOUN cana-3280 112 19 ( ( PUNCT cana-3280 112 20 b b NOUN cana-3280 112 21 ) ) PUNCT cana-3280 112 22 of of ADP cana-3280 112 23 activity activity NOUN cana-3280 112 24 i i PROPN cana-3280 112 25 d d PROPN cana-3280 112 26 fig.2 fig.2 PROPN cana-3280 112 27 and and CCONJ cana-3280 112 28 3 3 NUM cana-3280 112 29 show show VERB cana-3280 112 30 the the DET cana-3280 112 31 countplot countplot NOUN cana-3280 112 32 , , PUNCT cana-3280 112 33 histogram histogram NOUN cana-3280 112 34 , , PUNCT cana-3280 112 35 violin violin NOUN cana-3280 112 36 plot plot NOUN cana-3280 112 37 , , PUNCT cana-3280 112 38 and and CCONJ cana-3280 112 39 box box NOUN cana-3280 112 40 plot plot NOUN cana-3280 112 41 of of ADP cana-3280 112 42 activity activity NOUN cana-3280 112 43 i i X cana-3280 112 44 d. d. PROPN cana-3280 112 45 in in ADP cana-3280 112 46 the the DET cana-3280 112 47 histogram histogram NOUN cana-3280 112 48 , , PUNCT cana-3280 112 49 the the DET cana-3280 112 50 greatest great ADJ cana-3280 112 51 value value NOUN cana-3280 112 52 exhibited exhibit VERB cana-3280 112 53 is be AUX cana-3280 112 54 500 500 NUM cana-3280 112 55 , , PUNCT cana-3280 112 56 which which PRON cana-3280 112 57 is be AUX cana-3280 112 58 in in ADP cana-3280 112 59 the the DET cana-3280 112 60 range range NOUN cana-3280 112 61 of of ADP cana-3280 112 62 0 0 NUM cana-3280 112 63 to to PART cana-3280 112 64 10 10 NUM cana-3280 112 65 , , PUNCT cana-3280 112 66 and and CCONJ cana-3280 112 67 the the DET cana-3280 112 68 violin violin NOUN cana-3280 112 69 plot plot NOUN cana-3280 112 70 and and CCONJ cana-3280 112 71 box box NOUN cana-3280 112 72 plot plot NOUN cana-3280 112 73 indicate indicate VERB cana-3280 112 74 the the DET cana-3280 112 75 distribution distribution NOUN cana-3280 112 76 of of ADP cana-3280 112 77 the the DET cana-3280 112 78 values value NOUN cana-3280 112 79 . . PUNCT cana-3280 113 1 e. e. PROPN cana-3280 113 2 deep deep PROPN cana-3280 113 3 learning learning PROPN cana-3280 113 4 & & CCONJ cana-3280 113 5 modeling model VERB cana-3280 113 6 deep deep ADJ cana-3280 113 7 learning learning NOUN cana-3280 113 8 refers refer VERB cana-3280 113 9 to to ADP cana-3280 113 10 the the DET cana-3280 113 11 method method NOUN cana-3280 113 12 by by ADP cana-3280 113 13 which which PRON cana-3280 113 14 a a DET cana-3280 113 15 computer computer NOUN cana-3280 113 16 model model NOUN cana-3280 113 17 directly directly ADV cana-3280 113 18 picks pick VERB cana-3280 113 19 up up ADP cana-3280 113 20 categorization categorization NOUN cana-3280 113 21 skills skill NOUN cana-3280 113 22 from from ADP cana-3280 113 23 images image NOUN cana-3280 113 24 , , PUNCT cana-3280 113 25 text text NOUN cana-3280 113 26 , , PUNCT cana-3280 113 27 or or CCONJ cana-3280 113 28 sound sound ADJ cana-3280 113 29 . . PUNCT cana-3280 114 1 deep deep ADJ cana-3280 114 2 learning learning NOUN cana-3280 114 3 models model NOUN cana-3280 114 4 have have VERB cana-3280 114 5 the the DET cana-3280 114 6 potential potential NOUN cana-3280 114 7 to to PART cana-3280 114 8 achieve achieve VERB cana-3280 114 9 cutting cutting NOUN cana-3280 114 10 - - PUNCT cana-3280 114 11 edge edge NOUN cana-3280 114 12 accuracy accuracy NOUN cana-3280 114 13 , , PUNCT cana-3280 114 14 occasionally occasionally ADV cana-3280 114 15 surpassing surpass VERB cana-3280 114 16 human human ADJ cana-3280 114 17 performance performance NOUN cana-3280 114 18 . . PUNCT cana-3280 115 1 models model NOUN cana-3280 115 2 are be AUX cana-3280 115 3 trained train VERB cana-3280 115 4 using use VERB cana-3280 115 5 multi multi ADJ cana-3280 115 6 - - ADJ cana-3280 115 7 layer layer ADJ cana-3280 115 8 neural neural ADJ cana-3280 115 9 network network NOUN cana-3280 115 10 communications communication NOUN cana-3280 115 11 on on ADP cana-3280 115 12 applied apply VERB cana-3280 115 13 nonlinear nonlinear ADJ cana-3280 115 14 analysis analysis NOUN cana-3280 115 15 issn issn NOUN cana-3280 115 16 : : PUNCT cana-3280 115 17 1074 1074 NUM cana-3280 115 18 - - PUNCT cana-3280 115 19 133x 133x NUM cana-3280 115 20 vol vol NOUN cana-3280 115 21 32 32 NUM cana-3280 115 22 no no NOUN cana-3280 115 23 . . PUNCT cana-3280 116 1 6s 6s NUM cana-3280 116 2 ( ( PUNCT cana-3280 116 3 2025 2025 NUM cana-3280 116 4 ) ) PUNCT cana-3280 116 5 128 128 NUM cana-3280 116 6 https://internationalpubls.com https://internationalpubls.com X cana-3280 116 7 architectures architecture NOUN cana-3280 116 8 and and CCONJ cana-3280 116 9 a a DET cana-3280 116 10 massive massive ADJ cana-3280 116 11 quantity quantity NOUN cana-3280 116 12 of of ADP cana-3280 116 13 tagged tag VERB cana-3280 116 14 data datum NOUN cana-3280 116 15 . . PUNCT cana-3280 117 1 implement implement VERB cana-3280 117 2 two two NUM cana-3280 117 3 models model NOUN cana-3280 117 4 in in ADP cana-3280 117 5 this this DET cana-3280 117 6 work work NOUN cana-3280 117 7 , , PUNCT cana-3280 117 8 such such ADJ cana-3280 117 9 as as ADP cana-3280 117 10 2dcnn 2dcnn NUM cana-3280 117 11 lstm lstm PROPN cana-3280 117 12 & & CCONJ cana-3280 117 13 lrcn lrcn PROPN cana-3280 117 14 . . PUNCT cana-3280 118 1 β€’ β€’ NUM cana-3280 118 2 2dcnn 2dcnn NUM cana-3280 118 3 lstm lstm NOUN cana-3280 118 4 use use VERB cana-3280 118 5 convolutional convolutional ADJ cana-3280 118 6 lstm lstm ADJ cana-3280 118 7 2d 2d NOUN cana-3280 118 8 layers layer NOUN cana-3280 118 9 alongside alongside ADP cana-3280 118 10 kernel kernel PROPN cana-3280 118 11 size size NOUN cana-3280 118 12 3 3 PROPN cana-3280 118 13 * * SYM cana-3280 118 14 3 3 NUM cana-3280 118 15 , , PUNCT cana-3280 118 16 filter filter NOUN cana-3280 118 17 4 4 NUM cana-3280 118 18 , , PUNCT cana-3280 118 19 activation activation NOUN cana-3280 118 20 function function NOUN cana-3280 118 21 " " PUNCT cana-3280 118 22 tanh tanh NOUN cana-3280 118 23 , , PUNCT cana-3280 118 24 " " PUNCT cana-3280 118 25 recurrent recurrent ADJ cana-3280 118 26 dropout dropout NOUN cana-3280 118 27 20 20 NUM cana-3280 118 28 % % NOUN cana-3280 118 29 , , PUNCT cana-3280 118 30 input input NOUN cana-3280 118 31 shape shape NOUN cana-3280 118 32 64 64 NUM cana-3280 118 33 * * SYM cana-3280 118 34 64 64 NUM cana-3280 118 35 * * SYM cana-3280 118 36 3 3 NUM cana-3280 118 37 , , PUNCT cana-3280 118 38 and and CCONJ cana-3280 118 39 a a DET cana-3280 118 40 time time NOUN cana-3280 118 41 distributed distribute VERB cana-3280 118 42 dropout dropout NOUN cana-3280 118 43 with with ADP cana-3280 118 44 20 20 NUM cana-3280 118 45 % % NOUN cana-3280 118 46 to to PART cana-3280 118 47 create create VERB cana-3280 118 48 a a DET cana-3280 118 49 total total NOUN cana-3280 118 50 of of ADP cana-3280 118 51 4 4 NUM cana-3280 118 52 sections section NOUN cana-3280 118 53 layered layered ADJ cana-3280 118 54 architecture architecture NOUN cana-3280 118 55 . . PUNCT cana-3280 119 1 the the DET cana-3280 119 2 output output NOUN cana-3280 119 3 layer layer NOUN cana-3280 119 4 includes include VERB cana-3280 119 5 an an DET cana-3280 119 6 activation activation NOUN cana-3280 119 7 function function NOUN cana-3280 119 8 called call VERB cana-3280 119 9 softmax softmax NOUN cana-3280 119 10 . . PUNCT cana-3280 120 1 the the DET cana-3280 120 2 model model NOUN cana-3280 120 3 was be AUX cana-3280 120 4 built build VERB cana-3280 120 5 with with ADP cana-3280 120 6 categorical categorical ADJ cana-3280 120 7 cross cross ADJ cana-3280 120 8 - - ADJ cana-3280 120 9 entropy entropy ADJ cana-3280 120 10 loss loss NOUN cana-3280 120 11 , , PUNCT cana-3280 120 12 adam adam PROPN cana-3280 120 13 was be AUX cana-3280 120 14 the the DET cana-3280 120 15 optimizer optimizer NOUN cana-3280 120 16 , , PUNCT cana-3280 120 17 accuracy accuracy NOUN cana-3280 120 18 metrics metric NOUN cana-3280 120 19 were be AUX cana-3280 120 20 used use VERB cana-3280 120 21 , , PUNCT cana-3280 120 22 batch batch NOUN cana-3280 120 23 size size NOUN cana-3280 120 24 was be AUX cana-3280 120 25 16 16 NUM cana-3280 120 26 , , PUNCT cana-3280 120 27 validation validation NOUN cana-3280 120 28 split split NOUN cana-3280 120 29 was be AUX cana-3280 120 30 20 20 NUM cana-3280 120 31 % % NOUN cana-3280 120 32 , , PUNCT cana-3280 120 33 and and CCONJ cana-3280 120 34 epochs epoch NOUN cana-3280 120 35 were be AUX cana-3280 120 36 100 100 NUM cana-3280 120 37 . . PUNCT cana-3280 121 1 model model NOUN cana-3280 121 2 architecture architecture NOUN cana-3280 121 3 for for ADP cana-3280 121 4 the the DET cana-3280 121 5 2d 2d NUM cana-3280 121 6 cnn cnn PROPN cana-3280 121 7 lstm lstm PROPN cana-3280 121 8 [ [ X cana-3280 121 9 25 25 NUM cana-3280 121 10 ] ] PUNCT cana-3280 121 11 is be AUX cana-3280 121 12 shown show VERB cana-3280 121 13 in in ADP cana-3280 121 14 fig.4 fig.4 PROPN cana-3280 121 15 . . PUNCT cana-3280 122 1 equation equation NOUN cana-3280 122 2 2 2 NUM cana-3280 122 3 displays display VERB cana-3280 122 4 a a DET cana-3280 122 5 2d 2d NUM cana-3280 122 6 cnn cnn NOUN cana-3280 123 1 [ [ X cana-3280 123 2 26 26 NUM cana-3280 123 3 ] ] PUNCT cana-3280 123 4 , , PUNCT cana-3280 123 5 while while SCONJ cana-3280 123 6 equations equation NOUN cana-3280 123 7 3 3 NUM cana-3280 123 8 through through ADP cana-3280 123 9 8 8 NUM cana-3280 123 10 show show NOUN cana-3280 123 11 lstm lstm ADJ cana-3280 123 12 equations equation NOUN cana-3280 123 13 . . PUNCT cana-3280 124 1 fig.4 fig.4 PROPN cana-3280 124 2 2d 2d NUM cana-3280 124 3 cnn cnn PROPN cana-3280 124 4 lstm lstm PROPN cana-3280 124 5 model model NOUN cana-3280 124 6 architecture architecture NOUN cana-3280 124 7 π‘›π‘œπ‘’π‘‘ π‘›π‘œπ‘’π‘‘ NOUN cana-3280 124 8 = = X cana-3280 124 9 [ [ PUNCT cana-3280 124 10 𝑛𝑖𝑛+ 𝑛𝑖𝑛+ NOUN cana-3280 124 11 2π‘βˆ’π‘˜ 2π‘βˆ’π‘˜ NUM cana-3280 124 12 𝑠 𝑠 X cana-3280 124 13 ] ] PUNCT cana-3280 125 1 + + CCONJ cana-3280 125 2 1 1 NUM cana-3280 125 3 ( ( PUNCT cana-3280 125 4 2 2 NUM cana-3280 125 5 ) ) PUNCT cana-3280 125 6 𝑖𝑑 𝑖𝑑 NOUN cana-3280 125 7 = = SYM cana-3280 125 8 𝜎(π‘₯π‘‘π‘ˆπΌ 𝜎(π‘₯π‘‘π‘ˆπΌ PROPN cana-3280 125 9 + + CCONJ cana-3280 125 10 β„Žπ‘‘βˆ’1π‘Šπ‘– β„Žπ‘‘βˆ’1π‘Šπ‘– NOUN cana-3280 125 11 ) ) PUNCT cana-3280 125 12 ( ( PUNCT cana-3280 125 13 3 3 X cana-3280 125 14 ) ) PUNCT cana-3280 125 15 communications communication NOUN cana-3280 125 16 on on ADP cana-3280 125 17 applied apply VERB cana-3280 125 18 nonlinear nonlinear ADJ cana-3280 125 19 analysis analysis NOUN cana-3280 125 20 issn issn NOUN cana-3280 125 21 : : PUNCT cana-3280 125 22 1074 1074 NUM cana-3280 125 23 - - PUNCT cana-3280 125 24 133x 133x NUM cana-3280 125 25 vol vol NOUN cana-3280 125 26 32 32 NUM cana-3280 125 27 no no NOUN cana-3280 125 28 . . PUNCT cana-3280 126 1 6s 6s NUM cana-3280 126 2 ( ( PUNCT cana-3280 126 3 2025 2025 NUM cana-3280 126 4 ) ) PUNCT cana-3280 126 5 129 129 NUM cana-3280 127 1 https://internationalpubls.com https://internationalpubls.com X cana-3280 127 2 𝑓𝑑 𝑓𝑑 NOUN cana-3280 127 3 = = PUNCT cana-3280 127 4 𝜎(π‘₯π‘‘π‘ˆπ‘“ 𝜎(π‘₯π‘‘π‘ˆπ‘“ PROPN cana-3280 127 5 + + CCONJ cana-3280 127 6 β„Žπ‘‘βˆ’1π‘Šπ‘“ β„Žπ‘‘βˆ’1π‘Šπ‘“ PUNCT cana-3280 127 7 ) ) PUNCT cana-3280 127 8 ( ( PUNCT cana-3280 127 9 4 4 X cana-3280 127 10 ) ) PUNCT cana-3280 127 11 π‘œπ‘‘ π‘œπ‘‘ NOUN cana-3280 127 12 = = PUNCT cana-3280 127 13 𝜎(π‘₯π‘‘π‘ˆπ‘œ 𝜎(π‘₯π‘‘π‘ˆπ‘œ PRON cana-3280 127 14 + + NUM cana-3280 127 15 β„Žπ‘‘βˆ’1π‘Šπ‘œ β„Žπ‘‘βˆ’1π‘Šπ‘œ X cana-3280 127 16 ) ) PUNCT cana-3280 127 17 ( ( PUNCT cana-3280 127 18 5 5 X cana-3280 127 19 ) ) PUNCT cana-3280 127 20 οΏ½ οΏ½ PROPN cana-3280 127 21 Μƒ Μƒ NOUN cana-3280 127 22 οΏ½ οΏ½ NOUN cana-3280 127 23 𝑑 𝑑 ADJ cana-3280 127 24 = = SYM cana-3280 127 25 tanh tanh PROPN cana-3280 127 26 ( ( PUNCT cana-3280 127 27 π‘₯π‘‘π‘ˆπ‘” π‘₯π‘‘π‘ˆπ‘” PROPN cana-3280 127 28 + + CCONJ cana-3280 127 29 β„Žπ‘‘βˆ’1π‘Šπ‘” β„Žπ‘‘βˆ’1π‘Šπ‘” NUM cana-3280 127 30 ) ) PUNCT cana-3280 127 31 ( ( PUNCT cana-3280 127 32 6 6 X cana-3280 127 33 ) ) PUNCT cana-3280 127 34 𝐢𝑑 𝐢𝑑 PROPN cana-3280 127 35 = = PROPN cana-3280 127 36 𝜎(𝑓𝑑 𝜎(𝑓𝑑 PROPN cana-3280 127 37 βˆ— βˆ— NOUN cana-3280 127 38 πΆπ‘‘βˆ’1 πΆπ‘‘βˆ’1 PROPN cana-3280 127 39 + + CCONJ cana-3280 127 40 𝑖𝑑 𝑖𝑑 PROPN cana-3280 127 41 βˆ— βˆ— PROPN cana-3280 127 42 οΏ½ οΏ½ PROPN cana-3280 127 43 Μƒ Μƒ PROPN cana-3280 127 44 οΏ½ οΏ½ PROPN cana-3280 127 45 𝑑 𝑑 PROPN cana-3280 127 46 ( ( PUNCT cana-3280 127 47 7 7 NUM cana-3280 127 48 ) ) PUNCT cana-3280 127 49 β„Žπ‘‘ β„Žπ‘‘ NOUN cana-3280 127 50 = = SYM cana-3280 127 51 tanh(𝐢𝑑 tanh(𝐢𝑑 PROPN cana-3280 127 52 ) ) PUNCT cana-3280 127 53 βˆ— βˆ— NOUN cana-3280 127 54 π‘œπ‘‘ π‘œπ‘‘ PROPN cana-3280 127 55 ( ( PUNCT cana-3280 127 56 8) 8) NUM cana-3280 127 57 β€’ β€’ NUM cana-3280 127 58 long long ADJ cana-3280 127 59 - - PUNCT cana-3280 127 60 term term NOUN cana-3280 127 61 recurrent recurrent ADJ cana-3280 127 62 convolutional convolutional ADJ cana-3280 127 63 network network NOUN cana-3280 127 64 lrcn lrcn PROPN cana-3280 127 65 this this DET cana-3280 127 66 model model NOUN cana-3280 127 67 is be AUX cana-3280 127 68 built build VERB cana-3280 127 69 on on ADP cana-3280 127 70 2dcnn 2dcnn NUM cana-3280 127 71 and and CCONJ cana-3280 127 72 lstm lstm NOUN cana-3280 127 73 layers layer NOUN cana-3280 127 74 , , PUNCT cana-3280 127 75 each each DET cana-3280 127 76 layer layer NOUN cana-3280 127 77 of of ADP cana-3280 127 78 which which PRON cana-3280 127 79 is be AUX cana-3280 127 80 time time NOUN cana-3280 127 81 distributed distribute VERB cana-3280 127 82 . . PUNCT cana-3280 128 1 it it PRON cana-3280 128 2 also also ADV cana-3280 128 3 includes include VERB cana-3280 128 4 4 4 NUM cana-3280 128 5 convolutional convolutional ADJ cana-3280 128 6 layers layer NOUN cana-3280 128 7 , , PUNCT cana-3280 128 8 4 4 NUM cana-3280 128 9 max max NOUN cana-3280 128 10 - - PUNCT cana-3280 128 11 pooling pool VERB cana-3280 128 12 layers layer NOUN cana-3280 128 13 ( ( PUNCT cana-3280 128 14 2d 2d NUM cana-3280 128 15 time time NOUN cana-3280 128 16 distributed distribute VERB cana-3280 128 17 layers layer NOUN cana-3280 128 18 ) ) PUNCT cana-3280 128 19 , , PUNCT cana-3280 128 20 3 3 NUM cana-3280 128 21 dropout dropout NOUN cana-3280 128 22 layers layer NOUN cana-3280 128 23 with with ADP cana-3280 128 24 a a DET cana-3280 128 25 25 25 NUM cana-3280 128 26 % % NOUN cana-3280 128 27 dropout dropout NOUN cana-3280 128 28 rate rate NOUN cana-3280 128 29 , , PUNCT cana-3280 128 30 and and CCONJ cana-3280 128 31 a a DET cana-3280 128 32 dense dense ADJ cana-3280 128 33 layer layer NOUN cana-3280 128 34 serving serve VERB cana-3280 128 35 as as ADP cana-3280 128 36 the the DET cana-3280 128 37 output output NOUN cana-3280 128 38 layer layer NOUN cana-3280 128 39 with with ADP cana-3280 128 40 a a DET cana-3280 128 41 softmax softmax ADJ cana-3280 128 42 output output NOUN cana-3280 128 43 function function NOUN cana-3280 128 44 [ [ X cana-3280 128 45 16 16 NUM cana-3280 128 46 ] ] PUNCT cana-3280 128 47 . . PUNCT cana-3280 129 1 model model PROPN cana-3280 129 2 compile compile PROPN cana-3280 129 3 with with ADP cana-3280 129 4 adam adam PROPN cana-3280 129 5 's 's PART cana-3280 129 6 optimizer optimizer NOUN cana-3280 129 7 ; ; PUNCT cana-3280 129 8 metrics metric NOUN cana-3280 129 9 are be AUX cana-3280 129 10 accuracy accuracy ADJ cana-3280 129 11 , , PUNCT cana-3280 129 12 categorical categorical PROPN cana-3280 129 13 cross cross NOUN cana-3280 129 14 entropy entropy PROPN cana-3280 129 15 is be AUX cana-3280 129 16 the the DET cana-3280 129 17 loss loss NOUN cana-3280 129 18 ; ; PUNCT cana-3280 129 19 epochs epoch NOUN cana-3280 129 20 are be AUX cana-3280 129 21 100 100 NUM cana-3280 129 22 ; ; PUNCT cana-3280 129 23 batch batch NOUN cana-3280 129 24 size size NOUN cana-3280 129 25 is be AUX cana-3280 129 26 4 4 NUM cana-3280 129 27 ; ; PUNCT cana-3280 129 28 and and CCONJ cana-3280 129 29 the the DET cana-3280 129 30 validation validation NOUN cana-3280 129 31 split split NOUN cana-3280 129 32 is be AUX cana-3280 129 33 20 20 NUM cana-3280 129 34 % % NOUN cana-3280 129 35 . . PUNCT cana-3280 130 1 β€’ β€’ NUM cana-3280 130 2 creating create VERB cana-3280 130 3 a a DET cana-3280 130 4 transfer transfer NOUN cana-3280 130 5 learning learning NOUN cana-3280 130 6 model model NOUN cana-3280 130 7 when when SCONJ cana-3280 130 8 applying apply VERB cana-3280 130 9 training training NOUN cana-3280 130 10 over over ADP cana-3280 130 11 new new ADJ cana-3280 130 12 data datum NOUN cana-3280 130 13 , , PUNCT cana-3280 130 14 evaluate evaluate VERB cana-3280 130 15 the the DET cana-3280 130 16 performance performance NOUN cana-3280 130 17 of of ADP cana-3280 130 18 the the DET cana-3280 130 19 transfer transfer NOUN cana-3280 130 20 learning learn VERB cana-3280 130 21 model model NOUN cana-3280 130 22 with with ADP cana-3280 130 23 new new ADJ cana-3280 130 24 data datum NOUN cana-3280 130 25 videos video NOUN cana-3280 130 26 to to PART cana-3280 130 27 classify classify VERB cana-3280 130 28 and and CCONJ cana-3280 130 29 predict predict VERB cana-3280 130 30 the the DET cana-3280 130 31 classes class NOUN cana-3280 130 32 of of ADP cana-3280 130 33 a a DET cana-3280 130 34 new new ADJ cana-3280 130 35 dataset dataset NOUN cana-3280 130 36 . . PUNCT cana-3280 131 1 after after ADP cana-3280 131 2 training train VERB cana-3280 131 3 the the DET cana-3280 131 4 lrcn lrcn PROPN cana-3280 131 5 and and CCONJ cana-3280 131 6 2 2 NUM cana-3280 131 7 dcnn dcnn ADJ cana-3280 131 8 lstm lstm PROPN cana-3280 131 9 model model PROPN cana-3280 131 10 , , PUNCT cana-3280 131 11 save save VERB cana-3280 131 12 weights weight NOUN cana-3280 131 13 and and CCONJ cana-3280 131 14 load load VERB cana-3280 131 15 them they PRON cana-3280 131 16 in in ADP cana-3280 131 17 new new ADJ cana-3280 131 18 cells cell NOUN cana-3280 131 19 of of ADP cana-3280 131 20 code code NOUN cana-3280 131 21 . . PUNCT cana-3280 132 1 consider consider VERB cana-3280 132 2 the the DET cana-3280 132 3 same same ADJ cana-3280 132 4 process process NOUN cana-3280 132 5 of of ADP cana-3280 132 6 training training NOUN cana-3280 132 7 with with ADP cana-3280 132 8 the the DET cana-3280 132 9 same same ADJ cana-3280 132 10 number number NOUN cana-3280 132 11 of of ADP cana-3280 132 12 classes class NOUN cana-3280 132 13 but but CCONJ cana-3280 132 14 the the DET cana-3280 132 15 dataset dataset NOUN cana-3280 132 16 class class NOUN cana-3280 132 17 type type NOUN cana-3280 132 18 was be AUX cana-3280 132 19 changed change VERB cana-3280 132 20 . . PUNCT cana-3280 133 1 4 4 X cana-3280 133 2 . . X cana-3280 133 3 result result NOUN cana-3280 133 4 & & CCONJ cana-3280 133 5 discussion discussion VERB cana-3280 133 6 a a DET cana-3280 133 7 discussion discussion NOUN cana-3280 133 8 of of ADP cana-3280 133 9 the the DET cana-3280 133 10 outcomes outcome NOUN cana-3280 133 11 of of ADP cana-3280 133 12 the the DET cana-3280 133 13 deep deep ADJ cana-3280 133 14 learning learning NOUN cana-3280 133 15 models model NOUN cana-3280 133 16 is be AUX cana-3280 133 17 presented present VERB cana-3280 133 18 in in ADP cana-3280 133 19 this this DET cana-3280 133 20 section section NOUN cana-3280 133 21 . . PUNCT cana-3280 134 1 in in ADP cana-3280 134 2 this this DET cana-3280 134 3 work work NOUN cana-3280 134 4 , , PUNCT cana-3280 134 5 two two NUM cana-3280 134 6 models model NOUN cana-3280 134 7 are be AUX cana-3280 134 8 implemented implement VERB cana-3280 134 9 , , PUNCT cana-3280 134 10 including include VERB cana-3280 134 11 2dcnn 2dcnn NUM cana-3280 134 12 lstm lstm NOUN cana-3280 134 13 & & CCONJ cana-3280 134 14 lrcn lrcn PROPN cana-3280 134 15 , , PUNCT cana-3280 134 16 & & CCONJ cana-3280 134 17 the the DET cana-3280 134 18 metrics metric NOUN cana-3280 134 19 are be AUX cana-3280 134 20 accuracy accuracy NOUN cana-3280 134 21 or or CCONJ cana-3280 134 22 loss loss NOUN cana-3280 134 23 for for ADP cana-3280 134 24 human human ADJ cana-3280 134 25 activity activity NOUN cana-3280 134 26 recognition recognition NOUN cana-3280 134 27 . . PUNCT cana-3280 135 1 1 1 X cana-3280 135 2 ) ) PUNCT cana-3280 135 3 accuracy accuracy NOUN cana-3280 135 4 a a DET cana-3280 135 5 way way NOUN cana-3280 135 6 to to PART cana-3280 135 7 evaluate evaluate VERB cana-3280 135 8 the the DET cana-3280 135 9 effectiveness effectiveness NOUN cana-3280 135 10 of of ADP cana-3280 135 11 a a DET cana-3280 135 12 classification classification NOUN cana-3280 135 13 model model NOUN cana-3280 135 14 is be AUX cana-3280 135 15 accuracy accuracy NOUN cana-3280 135 16 . . PUNCT cana-3280 136 1 typically typically ADV cana-3280 136 2 , , PUNCT cana-3280 136 3 a a DET cana-3280 136 4 percentage percentage NOUN cana-3280 136 5 is be AUX cana-3280 136 6 used use VERB cana-3280 136 7 to to PART cana-3280 136 8 express express VERB cana-3280 136 9 it it PRON cana-3280 136 10 . . PUNCT cana-3280 137 1 accuracy accuracy NOUN cana-3280 137 2 is be AUX cana-3280 137 3 defined define VERB cana-3280 137 4 as as ADP cana-3280 137 5 the the DET cana-3280 137 6 percentage percentage NOUN cana-3280 137 7 of of ADP cana-3280 137 8 forecasts forecast NOUN cana-3280 137 9 when when SCONJ cana-3280 137 10 the the DET cana-3280 137 11 anticipated anticipate VERB cana-3280 137 12 value value NOUN cana-3280 137 13 and and CCONJ cana-3280 137 14 actual actual ADJ cana-3280 137 15 value value NOUN cana-3280 137 16 are be AUX cana-3280 137 17 equal equal ADJ cana-3280 137 18 . . PUNCT cana-3280 138 1 it it PRON cana-3280 138 2 is be AUX cana-3280 138 3 a a DET cana-3280 138 4 true true ADJ cana-3280 138 5 / / SYM cana-3280 138 6 false false ADJ cana-3280 138 7 binary binary ADJ cana-3280 138 8 value value NOUN cana-3280 138 9 for for ADP cana-3280 138 10 a a DET cana-3280 138 11 particular particular ADJ cana-3280 138 12 sample sample NOUN cana-3280 138 13 . . PUNCT cana-3280 139 1 graphing graph VERB cana-3280 139 2 and and CCONJ cana-3280 139 3 tracking tracking NOUN cana-3280 139 4 accuracy accuracy NOUN cana-3280 139 5 during during ADP cana-3280 139 6 the the DET cana-3280 139 7 training training NOUN cana-3280 139 8 process process NOUN cana-3280 139 9 are be AUX cana-3280 139 10 common common ADJ cana-3280 139 11 , , PUNCT cana-3280 139 12 even even ADV cana-3280 139 13 if if SCONJ cana-3280 139 14 the the DET cana-3280 139 15 number number NOUN cana-3280 139 16 is be AUX cana-3280 139 17 frequently frequently ADV cana-3280 139 18 connected connect VERB cana-3280 139 19 to to ADP cana-3280 139 20 the the DET cana-3280 139 21 overall overall ADJ cana-3280 139 22 or or CCONJ cana-3280 139 23 final final ADJ cana-3280 139 24 model model NOUN cana-3280 139 25 accuracy accuracy NOUN cana-3280 139 26 . . PUNCT cana-3280 140 1 accuracy accuracy NOUN cana-3280 140 2 may may AUX cana-3280 140 3 be be AUX cana-3280 140 4 measured measure VERB cana-3280 140 5 more more ADV cana-3280 140 6 easily easily ADV cana-3280 140 7 than than ADP cana-3280 140 8 loss loss NOUN cana-3280 140 9 . . PUNCT cana-3280 141 1 π΄π‘π‘π‘’π‘Ÿπ‘Žπ‘π‘¦ π΄π‘π‘π‘’π‘Ÿπ‘Žπ‘π‘¦ PROPN cana-3280 141 2 = = PRON cana-3280 141 3 ( ( PUNCT cana-3280 141 4 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝑇𝑁 PROPN cana-3280 141 5 ) ) PUNCT cana-3280 141 6 ( ( PUNCT cana-3280 141 7 𝑇𝑃+𝐹𝑃+𝑇𝑁+𝐹𝑁 𝑇𝑃+𝐹𝑃+𝑇𝑁+𝐹𝑁 PROPN cana-3280 141 8 ) ) PUNCT cana-3280 141 9 ( ( PUNCT cana-3280 141 10 9 9 NUM cana-3280 141 11 ) ) SYM cana-3280 141 12 2 2 NUM cana-3280 141 13 ) ) PUNCT cana-3280 141 14 loss loss NOUN cana-3280 141 15 a a DET cana-3280 141 16 loss loss NOUN cana-3280 141 17 function function NOUN cana-3280 141 18 sometimes sometimes ADV cana-3280 141 19 called call VERB cana-3280 141 20 a a DET cana-3280 141 21 cost cost NOUN cana-3280 141 22 function function NOUN cana-3280 141 23 , , PUNCT cana-3280 141 24 determines determine VERB cana-3280 141 25 a a DET cana-3280 141 26 forecast forecast NOUN cana-3280 141 27 's 's PART cana-3280 141 28 likelihood likelihood NOUN cana-3280 141 29 or or CCONJ cana-3280 141 30 level level NOUN cana-3280 141 31 of of ADP cana-3280 141 32 uncertainty uncertainty NOUN cana-3280 141 33 depending depend VERB cana-3280 141 34 on on ADP cana-3280 141 35 how how SCONJ cana-3280 141 36 far far ADV cana-3280 141 37 it it PRON cana-3280 141 38 differs differ VERB cana-3280 141 39 from from ADP cana-3280 141 40 the the DET cana-3280 141 41 true true ADJ cana-3280 141 42 value value NOUN cana-3280 141 43 . . PUNCT cana-3280 142 1 we we PRON cana-3280 142 2 can can AUX cana-3280 142 3 now now ADV cana-3280 142 4 see see VERB cana-3280 142 5 the the DET cana-3280 142 6 model model NOUN cana-3280 142 7 's 's PART cana-3280 142 8 performance performance NOUN cana-3280 142 9 in in ADP cana-3280 142 10 greater great ADJ cana-3280 142 11 detail detail NOUN cana-3280 142 12 . . PUNCT cana-3280 143 1 loss loss NOUN cana-3280 143 2 is be AUX cana-3280 143 3 the the DET cana-3280 143 4 sum sum NOUN cana-3280 143 5 of of ADP cana-3280 143 6 all all DET cana-3280 143 7 errors error NOUN cana-3280 143 8 created create VERB cana-3280 143 9 for for ADP cana-3280 143 10 each each DET cana-3280 143 11 sample sample NOUN cana-3280 143 12 in in ADP cana-3280 143 13 training training NOUN cana-3280 143 14 and and CCONJ cana-3280 143 15 validation validation NOUN cana-3280 143 16 sets set NOUN cana-3280 143 17 , , PUNCT cana-3280 143 18 as as SCONJ cana-3280 143 19 opposed oppose VERB cana-3280 143 20 to to ADP cana-3280 143 21 accuracy accuracy NOUN cana-3280 143 22 , , PUNCT cana-3280 143 23 which which PRON cana-3280 143 24 is be AUX cana-3280 143 25 expressed express VERB cana-3280 143 26 as as ADP cana-3280 143 27 a a DET cana-3280 143 28 percentage percentage NOUN cana-3280 143 29 . . PUNCT cana-3280 144 1 πΏπ‘œπ‘ π‘  πΏπ‘œπ‘ π‘  PROPN cana-3280 144 2 = = PUNCT cana-3280 144 3 βˆ’ βˆ’ PROPN cana-3280 144 4 1 1 NUM cana-3280 144 5 π‘š π‘š NOUN cana-3280 144 6 βˆ‘ βˆ‘ ADV cana-3280 145 1 𝒴𝑖 𝒴𝑖 PROPN cana-3280 145 2 βˆ™ βˆ™ PROPN cana-3280 145 3 π‘™π‘œπ‘”(π‘š π‘™π‘œπ‘”(π‘š PROPN cana-3280 145 4 𝑖=1 𝑖=1 PROPN cana-3280 145 5 𝒴 𝒴 PROPN cana-3280 145 6 οΏ½ οΏ½ PROPN cana-3280 145 7 Μ‚ Μ‚ NOUN cana-3280 145 8 οΏ½ οΏ½ NOUN cana-3280 145 9 ) ) PUNCT cana-3280 145 10 ( ( PUNCT cana-3280 145 11 10 10 NUM cana-3280 145 12 ) ) PUNCT cana-3280 145 13 table.1 table.1 VERB cana-3280 145 14 performance performance NOUN cana-3280 145 15 evaluation evaluation NOUN cana-3280 145 16 of of ADP cana-3280 145 17 models model NOUN cana-3280 145 18 model model NOUN cana-3280 145 19 training train VERB cana-3280 145 20 acc acc PROPN cana-3280 145 21 training train VERB cana-3280 145 22 loss loss NOUN cana-3280 145 23 validation validation NOUN cana-3280 145 24 acc acc PROPN cana-3280 145 25 validation validation NOUN cana-3280 145 26 loss loss NOUN cana-3280 146 1 2dcnnlstm 2dcnnlstm NUM cana-3280 146 2 98.34 98.34 NUM cana-3280 146 3 0.04 0.04 NUM cana-3280 146 4 54.41 54.41 NUM cana-3280 146 5 0.244 0.244 NUM cana-3280 146 6 lrcn lrcn PROPN cana-3280 146 7 99.08 99.08 NUM cana-3280 146 8 0.02 0.02 NUM cana-3280 146 9 66.91 66.91 NUM cana-3280 146 10 0.162 0.162 NUM cana-3280 146 11 transfer transfer NOUN cana-3280 146 12 2dcnnlstm 2dcnnlstm NUM cana-3280 146 13 100 100 NUM cana-3280 146 14 0.003 0.003 NUM cana-3280 146 15 47.62 47.62 NUM cana-3280 146 16 0.384 0.384 NUM cana-3280 146 17 transfer transfer NOUN cana-3280 146 18 lrcn lrcn PROPN cana-3280 146 19 100 100 NUM cana-3280 146 20 0.001 0.001 NUM cana-3280 146 21 69.39 69.39 NUM cana-3280 146 22 0.198 0.198 NUM cana-3280 146 23 communications communication NOUN cana-3280 146 24 on on ADP cana-3280 146 25 applied apply VERB cana-3280 146 26 nonlinear nonlinear ADJ cana-3280 146 27 analysis analysis NOUN cana-3280 146 28 issn issn NOUN cana-3280 146 29 : : PUNCT cana-3280 146 30 1074 1074 NUM cana-3280 146 31 - - PUNCT cana-3280 146 32 133x 133x NUM cana-3280 146 33 vol vol NOUN cana-3280 146 34 32 32 NUM cana-3280 146 35 no no NOUN cana-3280 146 36 . . PUNCT cana-3280 147 1 6s 6s NUM cana-3280 147 2 ( ( PUNCT cana-3280 147 3 2025 2025 NUM cana-3280 147 4 ) ) PUNCT cana-3280 147 5 130 130 NUM cana-3280 147 6 https://internationalpubls.com https://internationalpubls.com X cana-3280 148 1 the the DET cana-3280 148 2 performance performance NOUN cana-3280 148 3 evaluation evaluation NOUN cana-3280 148 4 of of ADP cana-3280 148 5 the the DET cana-3280 148 6 2dcnnlstm 2dcnnlstm NUM cana-3280 148 7 , , PUNCT cana-3280 148 8 transfer transfer VERB cana-3280 148 9 2dcnnlstm 2dcnnlstm NUM cana-3280 148 10 , , PUNCT cana-3280 148 11 lrcn lrcn PROPN cana-3280 148 12 , , PUNCT cana-3280 148 13 and and CCONJ cana-3280 148 14 transfer transfer VERB cana-3280 148 15 [ [ PRON cana-3280 148 16 27 27 NUM cana-3280 148 17 ] ] X cana-3280 148 18 lrcn lrcn PROPN cana-3280 148 19 is be AUX cana-3280 148 20 shown show VERB cana-3280 148 21 in in ADP cana-3280 148 22 table.1 table.1 PROPN cana-3280 148 23 , , PUNCT cana-3280 148 24 with with ADP cana-3280 148 25 the the DET cana-3280 148 26 maximum maximum ADJ cana-3280 148 27 training training NOUN cana-3280 148 28 accuracy accuracy NOUN cana-3280 148 29 and and CCONJ cana-3280 148 30 validation validation NOUN cana-3280 148 31 accuracy accuracy NOUN cana-3280 148 32 being be AUX cana-3280 148 33 achieved achieve VERB cana-3280 148 34 by by ADP cana-3280 148 35 the the DET cana-3280 148 36 transfer transfer NOUN cana-3280 148 37 lrcn lrcn PROPN cana-3280 148 38 at at ADP cana-3280 148 39 100 100 NUM cana-3280 148 40 and and CCONJ cana-3280 148 41 69.39 69.39 NUM cana-3280 148 42 , , PUNCT cana-3280 148 43 respectively respectively ADV cana-3280 148 44 . . PUNCT cana-3280 149 1 lowest low ADJ cana-3280 149 2 validation validation NOUN cana-3280 149 3 loss loss NOUN cana-3280 149 4 of of ADP cana-3280 149 5 0.162 0.162 NUM cana-3280 149 6 and and CCONJ cana-3280 149 7 lowest low ADJ cana-3280 149 8 training training NOUN cana-3280 149 9 loss loss NOUN cana-3280 149 10 of of ADP cana-3280 149 11 0.001 0.001 NUM cana-3280 149 12 respectively respectively ADV cana-3280 149 13 received receive VERB cana-3280 149 14 via via ADP cana-3280 149 15 transfer transfer NOUN cana-3280 149 16 lrcn lrcn PROPN cana-3280 149 17 . . PUNCT cana-3280 150 1 the the DET cana-3280 150 2 lowest low ADJ cana-3280 150 3 2dcnnlstm 2dcnnlstm NUM cana-3280 150 4 validation validation NOUN cana-3280 150 5 accuracy accuracy NOUN cana-3280 150 6 is be AUX cana-3280 150 7 47.62 47.62 NUM cana-3280 150 8 , , PUNCT cana-3280 150 9 whereas whereas SCONJ cana-3280 150 10 the the DET cana-3280 150 11 lowest low ADJ cana-3280 150 12 training training NOUN cana-3280 150 13 accuracy accuracy NOUN cana-3280 150 14 is be AUX cana-3280 150 15 98.34 98.34 NUM cana-3280 150 16 . . PUNCT cana-3280 151 1 transfer transfer NOUN cana-3280 151 2 lrcn lrcn PROPN cana-3280 151 3 successfully successfully ADV cana-3280 151 4 improved improve VERB cana-3280 151 5 2dcnn 2dcnn NUM cana-3280 151 6 lstm lstm NOUN cana-3280 151 7 by by ADP cana-3280 151 8 2 2 NUM cana-3280 151 9 % % NOUN cana-3280 151 10 . . PUNCT cana-3280 152 1 figure figure VERB cana-3280 152 2 4 4 NUM cana-3280 152 3 : : PUNCT cana-3280 152 4 accuracy accuracy NOUN cana-3280 152 5 graph graph NOUN cana-3280 152 6 of of ADP cana-3280 152 7 models model NOUN cana-3280 152 8 the the DET cana-3280 152 9 maximum maximum ADJ cana-3280 152 10 training training NOUN cana-3280 152 11 accuracy accuracy NOUN cana-3280 152 12 and and CCONJ cana-3280 152 13 validation validation NOUN cana-3280 152 14 accuracy accuracy NOUN cana-3280 152 15 were be AUX cana-3280 152 16 attained attain VERB cana-3280 152 17 by by ADP cana-3280 152 18 the the DET cana-3280 152 19 transfer transfer NOUN cana-3280 152 20 lrcn lrcn PROPN cana-3280 152 21 at at ADP cana-3280 152 22 100 100 NUM cana-3280 152 23 and and CCONJ cana-3280 152 24 69.39 69.39 NUM cana-3280 152 25 , , PUNCT cana-3280 152 26 respectively respectively ADV cana-3280 152 27 , , PUNCT cana-3280 152 28 in in ADP cana-3280 152 29 fig.5 fig.5 PROPN cana-3280 152 30 , , PUNCT cana-3280 152 31 which which PRON cana-3280 152 32 depicts depict VERB cana-3280 152 33 the the DET cana-3280 152 34 accuracy accuracy NOUN cana-3280 152 35 graph graph NOUN cana-3280 152 36 of of ADP cana-3280 152 37 deep deep ADJ cana-3280 152 38 learning learning NOUN cana-3280 152 39 and and CCONJ cana-3280 152 40 transfer transfer VERB cana-3280 152 41 learning learning NOUN cana-3280 152 42 models model NOUN cana-3280 152 43 such such ADJ cana-3280 152 44 as as ADP cana-3280 152 45 2dcnnlstm 2dcnnlstm NUM cana-3280 152 46 , , PUNCT cana-3280 152 47 lrcn lrcn PROPN cana-3280 152 48 , , PUNCT cana-3280 152 49 transfer transfer VERB cana-3280 152 50 2dcnnlstm 2dcnnlstm NUM cana-3280 152 51 , , PUNCT cana-3280 152 52 and and CCONJ cana-3280 152 53 transfer transfer VERB cana-3280 152 54 lrcn lrcn PROPN cana-3280 152 55 . . PUNCT cana-3280 153 1 figure figure VERB cana-3280 153 2 5 5 NUM cana-3280 153 3 : : PUNCT cana-3280 153 4 loss loss NOUN cana-3280 153 5 graph graph NOUN cana-3280 153 6 of of ADP cana-3280 153 7 models model NOUN cana-3280 153 8 the the DET cana-3280 153 9 loss loss NOUN cana-3280 153 10 graphs graph NOUN cana-3280 153 11 of of ADP cana-3280 153 12 deep deep ADJ cana-3280 153 13 learning learning NOUN cana-3280 153 14 and and CCONJ cana-3280 153 15 transfer transfer VERB cana-3280 153 16 learning learn VERB cana-3280 153 17 algorithms algorithm NOUN cana-3280 153 18 such such ADJ cana-3280 153 19 as as ADP cana-3280 153 20 2dcnnlstm 2dcnnlstm NUM cana-3280 153 21 , , PUNCT cana-3280 153 22 lrcn lrcn PROPN cana-3280 153 23 , , PUNCT cana-3280 153 24 transfer transfer VERB cana-3280 153 25 2dcnnlstm 2dcnnlstm NUM cana-3280 153 26 , , PUNCT cana-3280 153 27 and and CCONJ cana-3280 153 28 transfer transfer NOUN cana-3280 153 29 lrcn lrcn PROPN cana-3280 153 30 are be AUX cana-3280 153 31 shown show VERB cana-3280 153 32 in in ADP cana-3280 153 33 fig fig NOUN cana-3280 153 34 . . PUNCT cana-3280 154 1 6 6 NUM cana-3280 154 2 . . X cana-3280 154 3 lowest low ADJ cana-3280 154 4 validation validation NOUN cana-3280 154 5 loss loss NOUN cana-3280 154 6 of of ADP cana-3280 154 7 0.162 0.162 NUM cana-3280 154 8 & & CCONJ cana-3280 154 9 lowest low ADJ cana-3280 154 10 training training NOUN cana-3280 154 11 loss loss NOUN cana-3280 154 12 of of ADP cana-3280 154 13 0.001 0.001 NUM cana-3280 154 14 was be AUX cana-3280 154 15 obtained obtain VERB cana-3280 154 16 by by ADP cana-3280 154 17 transfer transfer NOUN cana-3280 154 18 lrcn lrcn PROPN cana-3280 154 19 , , PUNCT cana-3280 154 20 respectively respectively ADV cana-3280 154 21 . . PUNCT cana-3280 155 1 the the DET cana-3280 155 2 lowest low ADJ cana-3280 155 3 training training NOUN cana-3280 155 4 accuracy accuracy NOUN cana-3280 155 5 of of ADP cana-3280 155 6 the the DET cana-3280 155 7 2dcnnlstm 2dcnnlstm PROPN cana-3280 155 8 is be AUX cana-3280 155 9 98.34 98.34 NUM cana-3280 155 10 , , PUNCT cana-3280 155 11 while while SCONJ cana-3280 155 12 the the DET cana-3280 155 13 lowest low ADJ cana-3280 155 14 validation validation NOUN cana-3280 155 15 accuracy accuracy NOUN cana-3280 155 16 is be AUX cana-3280 155 17 47.62 47.62 NUM cana-3280 155 18 . . PUNCT cana-3280 156 1 ( ( PUNCT cana-3280 156 2 e e NOUN cana-3280 156 3 ) ) PUNCT cana-3280 156 4 ( ( PUNCT cana-3280 156 5 f f X cana-3280 156 6 ) ) PUNCT cana-3280 156 7 figure figure NOUN cana-3280 156 8 6 6 NUM cana-3280 156 9 : : PUNCT cana-3280 156 10 accuracy(e accuracy(e PROPN cana-3280 156 11 ) ) PUNCT cana-3280 156 12 and and CCONJ cana-3280 156 13 loss(f loss(f PROPN cana-3280 156 14 ) ) PUNCT cana-3280 156 15 of of ADP cana-3280 156 16 transfer transfer NOUN cana-3280 156 17 lrcn lrcn PROPN cana-3280 156 18 communications communication NOUN cana-3280 156 19 on on ADP cana-3280 156 20 applied apply VERB cana-3280 156 21 nonlinear nonlinear ADJ cana-3280 156 22 analysis analysis NOUN cana-3280 156 23 issn issn NOUN cana-3280 156 24 : : PUNCT cana-3280 156 25 1074 1074 NUM cana-3280 156 26 - - PUNCT cana-3280 156 27 133x 133x NUM cana-3280 156 28 vol vol NOUN cana-3280 156 29 32 32 NUM cana-3280 156 30 no no NOUN cana-3280 156 31 . . PUNCT cana-3280 157 1 6s 6s NUM cana-3280 157 2 ( ( PUNCT cana-3280 157 3 2025 2025 NUM cana-3280 157 4 ) ) PUNCT cana-3280 157 5 131 131 NUM cana-3280 158 1 https://internationalpubls.com https://internationalpubls.com X cana-3280 159 1 fig.7 fig.7 ADV cana-3280 159 2 is be AUX cana-3280 159 3 a a DET cana-3280 159 4 graphical graphical ADJ cana-3280 159 5 depiction depiction NOUN cana-3280 159 6 of of ADP cana-3280 159 7 the the DET cana-3280 159 8 best well ADV cana-3280 159 9 performed perform VERB cana-3280 159 10 model model NOUN cana-3280 159 11 's 's PART cana-3280 159 12 lrcn lrcn PROPN cana-3280 159 13 transfer transfer NOUN cana-3280 159 14 loss loss NOUN cana-3280 159 15 and and CCONJ cana-3280 159 16 accuracy accuracy NOUN cana-3280 159 17 , , PUNCT cana-3280 159 18 with with ADP cana-3280 159 19 accuracy accuracy NOUN cana-3280 159 20 considered consider VERB cana-3280 159 21 to to PART cana-3280 159 22 be be AUX cana-3280 159 23 represented represent VERB cana-3280 159 24 by by ADP cana-3280 159 25 the the DET cana-3280 159 26 color color NOUN cana-3280 159 27 blue blue NOUN cana-3280 159 28 and and CCONJ cana-3280 159 29 validation validation NOUN cana-3280 159 30 accuracy accuracy NOUN cana-3280 159 31 considered consider VERB cana-3280 159 32 to to PART cana-3280 159 33 be be AUX cana-3280 159 34 represented represent VERB cana-3280 159 35 by by ADP cana-3280 159 36 the the DET cana-3280 159 37 color color NOUN cana-3280 159 38 red red NOUN cana-3280 159 39 . . PUNCT cana-3280 160 1 in in ADP cana-3280 160 2 ( ( PUNCT cana-3280 160 3 f f X cana-3280 160 4 ) ) PUNCT cana-3280 160 5 , , PUNCT cana-3280 160 6 loss loss NOUN cana-3280 160 7 is be AUX cana-3280 160 8 represented represent VERB cana-3280 160 9 by by ADP cana-3280 160 10 the the DET cana-3280 160 11 color color NOUN cana-3280 160 12 blue blue NOUN cana-3280 160 13 , , PUNCT cana-3280 160 14 and and CCONJ cana-3280 160 15 validation validation NOUN cana-3280 160 16 loss loss NOUN cana-3280 160 17 is be AUX cana-3280 160 18 represented represent VERB cana-3280 160 19 by by ADP cana-3280 160 20 the the DET cana-3280 160 21 color color NOUN cana-3280 160 22 red red PROPN cana-3280 160 23 . . PUNCT cana-3280 161 1 figure figure NOUN cana-3280 161 2 7 7 NUM cana-3280 161 3 : : PUNCT cana-3280 161 4 confusion confusion NOUN cana-3280 161 5 matrix matrix NOUN cana-3280 161 6 of of ADP cana-3280 161 7 actual actual ADJ cana-3280 161 8 and and CCONJ cana-3280 161 9 predicted predict VERB cana-3280 161 10 variable variable ADJ cana-3280 161 11 β€’ β€’ NOUN cana-3280 161 12 research research NOUN cana-3280 161 13 gap gap NOUN cana-3280 161 14 research research NOUN cana-3280 161 15 on on ADP cana-3280 161 16 human human ADJ cana-3280 161 17 activity activity NOUN cana-3280 161 18 recognition recognition NOUN cana-3280 161 19 ( ( PUNCT cana-3280 161 20 har har NOUN cana-3280 161 21 ) ) PUNCT cana-3280 161 22 through through ADP cana-3280 161 23 video video NOUN cana-3280 161 24 surveillance surveillance NOUN cana-3280 161 25 has have AUX cana-3280 161 26 become become VERB cana-3280 161 27 popular popular ADJ cana-3280 161 28 , , PUNCT cana-3280 161 29 particularly particularly ADV cana-3280 161 30 in in ADP cana-3280 161 31 inpatient inpatient NOUN cana-3280 161 32 rehabilitation rehabilitation NOUN cana-3280 161 33 and and CCONJ cana-3280 161 34 mobile mobile ADJ cana-3280 161 35 health health NOUN cana-3280 161 36 monitoring monitoring NOUN cana-3280 161 37 . . PUNCT cana-3280 162 1 there there PRON cana-3280 162 2 is be VERB cana-3280 162 3 a a DET cana-3280 162 4 notable notable ADJ cana-3280 162 5 gap gap NOUN cana-3280 162 6 in in ADP cana-3280 162 7 effectively effectively ADV cana-3280 162 8 adapting adapt VERB cana-3280 162 9 human human ADJ cana-3280 162 10 activity activity NOUN cana-3280 162 11 recognition recognition NOUN cana-3280 162 12 ( ( PUNCT cana-3280 162 13 har har NOUN cana-3280 162 14 ) ) PUNCT cana-3280 162 15 classifiers classifier NOUN cana-3280 162 16 to to ADP cana-3280 162 17 new new ADJ cana-3280 162 18 users user NOUN cana-3280 162 19 due due ADP cana-3280 162 20 to to ADP cana-3280 162 21 variations variation NOUN cana-3280 162 22 in in ADP cana-3280 162 23 activity activity NOUN cana-3280 162 24 patterns pattern NOUN cana-3280 162 25 that that PRON cana-3280 162 26 can can AUX cana-3280 162 27 impact impact VERB cana-3280 162 28 accuracy accuracy NOUN cana-3280 162 29 . . PUNCT cana-3280 163 1 the the DET cana-3280 163 2 paper paper NOUN cana-3280 163 3 presents present VERB cana-3280 163 4 various various ADJ cana-3280 163 5 deep deep ADJ cana-3280 163 6 learning learning NOUN cana-3280 163 7 models model NOUN cana-3280 163 8 such such ADJ cana-3280 163 9 as as ADP cana-3280 163 10 lrcn lrcn PROPN cana-3280 163 11 , , PUNCT cana-3280 163 12 transfer transfer VERB cana-3280 163 13 lrcn lrcn PROPN cana-3280 163 14 , , PUNCT cana-3280 163 15 2dcnnlstm 2dcnnlstm NUM cana-3280 163 16 , , PUNCT cana-3280 163 17 and and CCONJ cana-3280 163 18 lrcn lrcn PROPN cana-3280 163 19 , , PUNCT cana-3280 163 20 emphasizing emphasize VERB cana-3280 163 21 the the DET cana-3280 163 22 superior superior ADJ cana-3280 163 23 performance performance NOUN cana-3280 163 24 of of ADP cana-3280 163 25 transfer transfer NOUN cana-3280 163 26 lrcn lrcn PROPN cana-3280 163 27 in in ADP cana-3280 163 28 terms term NOUN cana-3280 163 29 of of ADP cana-3280 163 30 high high ADJ cana-3280 163 31 accuracy accuracy NOUN cana-3280 163 32 scores score NOUN cana-3280 163 33 . . PUNCT cana-3280 164 1 further further ADJ cana-3280 164 2 study study NOUN cana-3280 164 3 is be AUX cana-3280 164 4 required require VERB cana-3280 164 5 to to PART cana-3280 164 6 improve improve VERB cana-3280 164 7 flexibility flexibility NOUN cana-3280 164 8 and and CCONJ cana-3280 164 9 efficiency efficiency NOUN cana-3280 164 10 in in ADP cana-3280 164 11 real real ADJ cana-3280 164 12 - - PUNCT cana-3280 164 13 world world NOUN cana-3280 164 14 situations situation NOUN cana-3280 164 15 , , PUNCT cana-3280 164 16 notwithstanding notwithstanding ADP cana-3280 164 17 current current ADJ cana-3280 164 18 improvements improvement NOUN cana-3280 164 19 . . PUNCT cana-3280 165 1 β€’ β€’ NUM cana-3280 165 2 contribution contribution NOUN cana-3280 165 3 of of ADP cana-3280 165 4 the the DET cana-3280 165 5 study study NOUN cana-3280 165 6 this this DET cana-3280 165 7 study study NOUN cana-3280 165 8 makes make VERB cana-3280 165 9 an an DET cana-3280 165 10 important important ADJ cana-3280 165 11 contribution contribution NOUN cana-3280 165 12 to to ADP cana-3280 165 13 the the DET cana-3280 165 14 advancement advancement NOUN cana-3280 165 15 of of ADP cana-3280 165 16 human human ADJ cana-3280 165 17 activity activity NOUN cana-3280 165 18 recognition recognition NOUN cana-3280 165 19 ( ( PUNCT cana-3280 165 20 har har NOUN cana-3280 165 21 ) ) PUNCT cana-3280 165 22 using use VERB cana-3280 165 23 video video NOUN cana-3280 165 24 surveillance surveillance NOUN cana-3280 165 25 , , PUNCT cana-3280 165 26 especially especially ADV cana-3280 165 27 in in ADP cana-3280 165 28 hospital hospital NOUN cana-3280 165 29 environments environment NOUN cana-3280 165 30 . . PUNCT cana-3280 166 1 the the DET cana-3280 166 2 research research NOUN cana-3280 166 3 addresses address VERB cana-3280 166 4 the the DET cana-3280 166 5 difficulty difficulty NOUN cana-3280 166 6 of of ADP cana-3280 166 7 adapting adapt VERB cana-3280 166 8 har har NOUN cana-3280 166 9 classifiers classifier NOUN cana-3280 166 10 to to ADP cana-3280 166 11 new new ADJ cana-3280 166 12 users user NOUN cana-3280 166 13 by by ADP cana-3280 166 14 introducing introduce VERB cana-3280 166 15 and and CCONJ cana-3280 166 16 evaluating evaluate VERB cana-3280 166 17 deep deep ADJ cana-3280 166 18 learning learning NOUN cana-3280 166 19 models model NOUN cana-3280 166 20 including include VERB cana-3280 166 21 lrcn lrcn PROPN cana-3280 166 22 , , PUNCT cana-3280 166 23 transfer transfer VERB cana-3280 166 24 lrcn lrcn PROPN cana-3280 166 25 , , PUNCT cana-3280 166 26 and and CCONJ cana-3280 166 27 2dcnnlstm 2dcnnlstm NUM cana-3280 166 28 . . PUNCT cana-3280 167 1 the the DET cana-3280 167 2 remarkable remarkable ADJ cana-3280 167 3 achievement achievement NOUN cana-3280 167 4 of of ADP cana-3280 167 5 transfer transfer NOUN cana-3280 167 6 lrcn lrcn PROPN cana-3280 167 7 is be AUX cana-3280 167 8 shown show VERB cana-3280 167 9 in in ADP cana-3280 167 10 its its PRON cana-3280 167 11 exceptional exceptional ADJ cana-3280 167 12 performance performance NOUN cana-3280 167 13 , , PUNCT cana-3280 167 14 achieving achieve VERB cana-3280 167 15 the the DET cana-3280 167 16 top top ADJ cana-3280 167 17 scores score NOUN cana-3280 167 18 for for ADP cana-3280 167 19 training training NOUN cana-3280 167 20 and and CCONJ cana-3280 167 21 validation validation NOUN cana-3280 167 22 accuracy accuracy NOUN cana-3280 167 23 . . PUNCT cana-3280 168 1 this this DET cana-3280 168 2 discovery discovery NOUN cana-3280 168 3 is be AUX cana-3280 168 4 essential essential ADJ cana-3280 168 5 for for ADP cana-3280 168 6 practical practical ADJ cana-3280 168 7 uses use NOUN cana-3280 168 8 , , PUNCT cana-3280 168 9 guaranteeing guarantee VERB cana-3280 168 10 reliable reliable ADJ cana-3280 168 11 identification identification NOUN cana-3280 168 12 of of ADP cana-3280 168 13 activities activity NOUN cana-3280 168 14 even even ADV cana-3280 168 15 in in ADP cana-3280 168 16 the the DET cana-3280 168 17 presence presence NOUN cana-3280 168 18 of of ADP cana-3280 168 19 various various ADJ cana-3280 168 20 user user NOUN cana-3280 168 21 behaviours behaviour NOUN cana-3280 168 22 . . PUNCT cana-3280 169 1 the the DET cana-3280 169 2 study study NOUN cana-3280 169 3 's 's PART cana-3280 169 4 findings finding NOUN cana-3280 169 5 will will AUX cana-3280 169 6 facilitate facilitate VERB cana-3280 169 7 the the DET cana-3280 169 8 improved improved ADJ cana-3280 169 9 utilization utilization NOUN cana-3280 169 10 of of ADP cana-3280 169 11 har har NOUN cana-3280 169 12 in in ADP cana-3280 169 13 inpatient inpatient PROPN cana-3280 169 14 rehabilitation rehabilitation NOUN cana-3280 169 15 , , PUNCT cana-3280 169 16 activity activity NOUN cana-3280 169 17 recognition recognition NOUN cana-3280 169 18 , , PUNCT cana-3280 169 19 and and CCONJ cana-3280 169 20 mobile mobile ADJ cana-3280 169 21 health health NOUN cana-3280 169 22 monitoring monitoring NOUN cana-3280 169 23 , , PUNCT cana-3280 169 24 ensuring ensure VERB cana-3280 169 25 better well ADJ cana-3280 169 26 accuracy accuracy NOUN cana-3280 169 27 and and CCONJ cana-3280 169 28 flexibility flexibility NOUN cana-3280 169 29 . . PUNCT cana-3280 170 1 β€’ β€’ NUM cana-3280 170 2 limitation limitation NOUN cana-3280 170 3 although although SCONJ cana-3280 170 4 this this DET cana-3280 170 5 study study NOUN cana-3280 170 6 has have AUX cana-3280 170 7 made make VERB cana-3280 170 8 progress progress NOUN cana-3280 170 9 , , PUNCT cana-3280 170 10 it it PRON cana-3280 170 11 nevertheless nevertheless ADV cana-3280 170 12 has have VERB cana-3280 170 13 constraints constraint NOUN cana-3280 170 14 . . PUNCT cana-3280 171 1 relying rely VERB cana-3280 171 2 on on ADP cana-3280 171 3 current current ADJ cana-3280 171 4 data datum NOUN cana-3280 171 5 for for ADP cana-3280 171 6 offline offline ADJ cana-3280 171 7 training training NOUN cana-3280 171 8 could could AUX cana-3280 171 9 impede impede VERB cana-3280 171 10 the the DET cana-3280 171 11 ability ability NOUN cana-3280 171 12 to to PART cana-3280 171 13 adjust adjust VERB cana-3280 171 14 to to ADP cana-3280 171 15 distinct distinct ADJ cana-3280 171 16 user user NOUN cana-3280 171 17 behaviour behaviour NOUN cana-3280 171 18 patterns pattern NOUN cana-3280 171 19 . . PUNCT cana-3280 172 1 the the DET cana-3280 172 2 study study NOUN cana-3280 172 3 mainly mainly ADV cana-3280 172 4 concentrates concentrate VERB cana-3280 172 5 on on ADP cana-3280 172 6 a a DET cana-3280 172 7 certain certain ADJ cana-3280 172 8 group group NOUN cana-3280 172 9 of of ADP cana-3280 172 10 deep deep ADJ cana-3280 172 11 learning learning NOUN cana-3280 172 12 models model NOUN cana-3280 172 13 , , PUNCT cana-3280 172 14 possibly possibly ADV cana-3280 172 15 overlooking overlook VERB cana-3280 172 16 other other ADJ cana-3280 172 17 effective effective ADJ cana-3280 172 18 methods method NOUN cana-3280 172 19 . . PUNCT cana-3280 173 1 furthermore furthermore ADV cana-3280 173 2 , , PUNCT cana-3280 173 3 the the DET cana-3280 173 4 great great ADJ cana-3280 173 5 accuracy accuracy NOUN cana-3280 173 6 of of ADP cana-3280 173 7 transfer transfer NOUN cana-3280 173 8 lrcn lrcn PROPN cana-3280 173 9 may may AUX cana-3280 173 10 not not PART cana-3280 173 11 always always ADV cana-3280 173 12 be be AUX cana-3280 173 13 applicable applicable ADJ cana-3280 173 14 in in ADP cana-3280 173 15 real real ADJ cana-3280 173 16 - - PUNCT cana-3280 173 17 world world NOUN cana-3280 173 18 situations situation NOUN cana-3280 173 19 characterized characterize VERB cana-3280 173 20 by by ADP cana-3280 173 21 dynamic dynamic ADJ cana-3280 173 22 and and CCONJ cana-3280 173 23 unpredictable unpredictable ADJ cana-3280 173 24 behaviours behaviour NOUN cana-3280 173 25 . . PUNCT cana-3280 174 1 the the DET cana-3280 174 2 study study NOUN cana-3280 174 3 focuses focus VERB cana-3280 174 4 on on ADP cana-3280 174 5 specific specific ADJ cana-3280 174 6 models model NOUN cana-3280 174 7 and and CCONJ cana-3280 174 8 does do AUX cana-3280 174 9 not not PART cana-3280 174 10 extensively extensively ADV cana-3280 174 11 investigate investigate VERB cana-3280 174 12 wider wide ADJ cana-3280 174 13 factors factor NOUN cana-3280 174 14 like like ADP cana-3280 174 15 computational computational ADJ cana-3280 174 16 costs cost NOUN cana-3280 174 17 and and CCONJ cana-3280 174 18 real real ADJ cana-3280 174 19 - - PUNCT cana-3280 174 20 time time NOUN cana-3280 174 21 processing processing NOUN cana-3280 174 22 capabilities capability NOUN cana-3280 174 23 . . PUNCT cana-3280 175 1 these these DET cana-3280 175 2 constraints constraint NOUN cana-3280 175 3 underscore underscore VERB cana-3280 175 4 the the DET cana-3280 175 5 necessity necessity NOUN cana-3280 175 6 for for ADP cana-3280 175 7 a a DET cana-3280 175 8 thorough thorough ADJ cana-3280 175 9 and and CCONJ cana-3280 175 10 pragmatic pragmatic ADJ cana-3280 175 11 strategy strategy NOUN cana-3280 175 12 to to PART cana-3280 175 13 tackle tackle VERB cana-3280 175 14 the the DET cana-3280 175 15 intricacies intricacy NOUN cana-3280 175 16 of of ADP cana-3280 175 17 various various ADJ cana-3280 175 18 user user NOUN cana-3280 175 19 behaviours behaviour NOUN cana-3280 175 20 in in ADP cana-3280 175 21 future future ADJ cana-3280 175 22 study study NOUN cana-3280 175 23 . . PUNCT cana-3280 176 1 communications communication NOUN cana-3280 176 2 on on ADP cana-3280 176 3 applied apply VERB cana-3280 176 4 nonlinear nonlinear ADJ cana-3280 176 5 analysis analysis NOUN cana-3280 176 6 issn issn NOUN cana-3280 176 7 : : PUNCT cana-3280 176 8 1074 1074 NUM cana-3280 176 9 - - PUNCT cana-3280 176 10 133x 133x NUM cana-3280 176 11 vol vol NOUN cana-3280 176 12 32 32 NUM cana-3280 176 13 no no NOUN cana-3280 176 14 . . PUNCT cana-3280 177 1 6s 6s NUM cana-3280 177 2 ( ( PUNCT cana-3280 177 3 2025 2025 NUM cana-3280 177 4 ) ) PUNCT cana-3280 177 5 132 132 NUM cana-3280 177 6 https://internationalpubls.com https://internationalpubls.com X cana-3280 177 7 5 5 NUM cana-3280 177 8 . . X cana-3280 177 9 conclusion conclusion NOUN cana-3280 177 10 human human ADJ cana-3280 177 11 activity activity NOUN cana-3280 177 12 recognition recognition NOUN cana-3280 177 13 ( ( PUNCT cana-3280 177 14 har har NOUN cana-3280 177 15 ) ) PUNCT cana-3280 177 16 using use VERB cana-3280 177 17 video video NOUN cana-3280 177 18 surveillance surveillance NOUN cana-3280 177 19 has have AUX cana-3280 177 20 recently recently ADV cana-3280 177 21 witnessed witness VERB cana-3280 177 22 an an DET cana-3280 177 23 increase increase NOUN cana-3280 177 24 in in ADP cana-3280 177 25 popularity popularity NOUN cana-3280 177 26 across across ADP cana-3280 177 27 the the DET cana-3280 177 28 board board NOUN cana-3280 177 29 , , PUNCT cana-3280 177 30 including include VERB cana-3280 177 31 inpatient inpatient NOUN cana-3280 177 32 rehabilitation rehabilitation NOUN cana-3280 177 33 , , PUNCT cana-3280 177 34 activity activity NOUN cana-3280 177 35 recognition recognition NOUN cana-3280 177 36 , , PUNCT cana-3280 177 37 and and CCONJ cana-3280 177 38 mobile mobile ADJ cana-3280 177 39 health health NOUN cana-3280 177 40 monitoring monitoring NOUN cana-3280 177 41 . . PUNCT cana-3280 178 1 activities activity NOUN cana-3280 178 2 recognise recognise VERB cana-3280 178 3 by by ADP cana-3280 178 4 videos video NOUN cana-3280 178 5 , , PUNCT cana-3280 178 6 before before ADP cana-3280 178 7 being be AUX cana-3280 178 8 applied apply VERB cana-3280 178 9 to to ADP cana-3280 178 10 new new ADJ cana-3280 178 11 users user NOUN cana-3280 178 12 , , PUNCT cana-3280 178 13 a a DET cana-3280 178 14 har har NOUN cana-3280 178 15 classifier classifier NOUN cana-3280 178 16 is be AUX cana-3280 178 17 often often ADV cana-3280 178 18 trained train VERB cana-3280 178 19 offline offline ADJ cana-3280 178 20 with with ADP cana-3280 178 21 known know VERB cana-3280 178 22 users user NOUN cana-3280 178 23 . . PUNCT cana-3280 179 1 the the DET cana-3280 179 2 accuracy accuracy NOUN cana-3280 179 3 of of ADP cana-3280 179 4 this this DET cana-3280 179 5 strategy strategy NOUN cana-3280 179 6 may may AUX cana-3280 179 7 be be AUX cana-3280 179 8 compromised compromise VERB cana-3280 179 9 if if SCONJ cana-3280 179 10 the the DET cana-3280 179 11 activity activity NOUN cana-3280 179 12 patterns pattern NOUN cana-3280 179 13 of of ADP cana-3280 179 14 new new ADJ cana-3280 179 15 users user NOUN cana-3280 179 16 differ differ VERB cana-3280 179 17 from from ADP cana-3280 179 18 those those PRON cana-3280 179 19 in in ADP cana-3280 179 20 the the DET cana-3280 179 21 training training NOUN cana-3280 179 22 data datum NOUN cana-3280 179 23 . . PUNCT cana-3280 180 1 it it PRON cana-3280 180 2 is be AUX cana-3280 180 3 impractical impractical ADJ cana-3280 180 4 to to PART cana-3280 180 5 create create VERB cana-3280 180 6 mobile mobile ADJ cana-3280 180 7 applications application NOUN cana-3280 180 8 from from ADP cana-3280 180 9 scratch scratch NOUN cana-3280 180 10 because because SCONJ cana-3280 180 11 of of ADP cana-3280 180 12 the the DET cana-3280 180 13 high high ADJ cana-3280 180 14 cost cost NOUN cana-3280 180 15 of of ADP cana-3280 180 16 computing computing NOUN cana-3280 180 17 and and CCONJ cana-3280 180 18 the the DET cana-3280 180 19 extensive extensive ADJ cana-3280 180 20 learning learning NOUN cana-3280 180 21 curve curve NOUN cana-3280 180 22 for for ADP cana-3280 180 23 new new ADJ cana-3280 180 24 users user NOUN cana-3280 180 25 . . PUNCT cana-3280 181 1 the the DET cana-3280 181 2 lrcn lrcn PROPN cana-3280 181 3 , , PUNCT cana-3280 181 4 transfer transfer VERB cana-3280 181 5 lrcn lrcn PROPN cana-3280 181 6 , , PUNCT cana-3280 181 7 2dcnnlstm 2dcnnlstm NUM cana-3280 181 8 , , PUNCT cana-3280 181 9 and and CCONJ cana-3280 181 10 lrcn lrcn PROPN cana-3280 181 11 models model NOUN cana-3280 181 12 were be AUX cana-3280 181 13 among among ADP cana-3280 181 14 the the DET cana-3280 181 15 deep deep ADJ cana-3280 181 16 learning learning NOUN cana-3280 181 17 or or CCONJ cana-3280 181 18 transfer transfer VERB cana-3280 181 19 learning learning NOUN cana-3280 181 20 models model NOUN cana-3280 181 21 proposed propose VERB cana-3280 181 22 in in ADP cana-3280 181 23 this this DET cana-3280 181 24 study study NOUN cana-3280 181 25 . . PUNCT cana-3280 182 1 with with ADP cana-3280 182 2 scores score NOUN cana-3280 182 3 of of ADP cana-3280 182 4 100 100 NUM cana-3280 182 5 and and CCONJ cana-3280 182 6 69.39 69.39 NUM cana-3280 182 7 , , PUNCT cana-3280 182 8 the the DET cana-3280 182 9 transfer transfer NOUN cana-3280 182 10 lrcn lrcn PROPN cana-3280 182 11 had have VERB cana-3280 182 12 the the DET cana-3280 182 13 greatest great ADJ cana-3280 182 14 training training NOUN cana-3280 182 15 accuracy accuracy NOUN cana-3280 182 16 and and CCONJ cana-3280 182 17 validation validation NOUN cana-3280 182 18 accuracy accuracy NOUN cana-3280 182 19 . . PUNCT cana-3280 183 1 the the DET cana-3280 183 2 lowest low ADJ cana-3280 183 3 validation validation NOUN cana-3280 183 4 loss loss NOUN cana-3280 183 5 and and CCONJ cana-3280 183 6 lowest low ADJ cana-3280 183 7 training training NOUN cana-3280 183 8 loss loss NOUN cana-3280 183 9 , , PUNCT cana-3280 183 10 each each PRON cana-3280 183 11 of of ADP cana-3280 183 12 which which PRON cana-3280 183 13 was be AUX cana-3280 183 14 received receive VERB cana-3280 183 15 via via ADP cana-3280 183 16 transfer transfer NOUN cana-3280 183 17 lrcn lrcn PROPN cana-3280 183 18 , , PUNCT cana-3280 183 19 were be AUX cana-3280 183 20 0.16 0.16 NUM cana-3280 183 21 and and CCONJ cana-3280 183 22 0.001 0.001 NUM cana-3280 183 23 respectively respectively ADV cana-3280 183 24 . . PUNCT cana-3280 184 1 while while SCONJ cana-3280 184 2 the the DET cana-3280 184 3 2dcnnlstm 2dcnnlstm PROPN cana-3280 184 4 's 's PART cana-3280 184 5 lowest low ADJ cana-3280 184 6 validation validation NOUN cana-3280 184 7 accuracy accuracy NOUN cana-3280 184 8 is be AUX cana-3280 184 9 47.62 47.62 NUM cana-3280 184 10 , , PUNCT cana-3280 184 11 its its PRON cana-3280 184 12 lowest low ADJ cana-3280 184 13 training training NOUN cana-3280 184 14 accuracy accuracy NOUN cana-3280 184 15 is be AUX cana-3280 184 16 98.34 98.34 NUM cana-3280 184 17 . . PUNCT cana-3280 185 1 2 2 NUM cana-3280 185 2 % % NOUN cana-3280 185 3 of of ADP cana-3280 185 4 the the DET cana-3280 185 5 2dcnn 2dcnn NUM cana-3280 185 6 lstm lstm NOUN cana-3280 185 7 was be AUX cana-3280 185 8 successfully successfully ADV cana-3280 185 9 increased increase VERB cana-3280 185 10 by by ADP cana-3280 185 11 transfer transfer NOUN cana-3280 185 12 lrcn lrcn PROPN cana-3280 185 13 . . PUNCT cana-3280 186 1 refrences refrence VERB cana-3280 187 1 [ [ X cana-3280 187 2 1 1 X cana-3280 187 3 ] ] X cana-3280 187 4 n. n. PROPN cana-3280 187 5 halim halim PROPN cana-3280 187 6 , , PUNCT cana-3280 187 7 β€œ " PUNCT cana-3280 187 8 stochastic stochastic ADJ cana-3280 187 9 recognition recognition NOUN cana-3280 187 10 of of ADP cana-3280 187 11 human human ADJ cana-3280 187 12 daily daily ADJ cana-3280 187 13 activities activity NOUN cana-3280 187 14 via via ADP cana-3280 187 15 hybrid hybrid ADJ cana-3280 187 16 descriptors descriptor NOUN cana-3280 187 17 and and CCONJ cana-3280 187 18 random random ADJ cana-3280 187 19 forest forest NOUN cana-3280 187 20 using use VERB cana-3280 187 21 wearable wearable ADJ cana-3280 187 22 sensors sensor NOUN cana-3280 187 23 , , PUNCT cana-3280 187 24 ” " PUNCT cana-3280 187 25 array array NOUN cana-3280 187 26 , , PUNCT cana-3280 187 27 vol vol NOUN cana-3280 187 28 . . PROPN cana-3280 187 29 15 15 NUM cana-3280 187 30 , , PUNCT cana-3280 187 31 no no INTJ cana-3280 187 32 . . PUNCT cana-3280 188 1 may may AUX cana-3280 188 2 , , PUNCT cana-3280 188 3 p. p. NOUN cana-3280 188 4 100190 100190 NUM cana-3280 188 5 , , PUNCT cana-3280 188 6 2022 2022 NUM cana-3280 188 7 , , PUNCT cana-3280 188 8 doi doi NOUN cana-3280 188 9 : : PUNCT cana-3280 188 10 10.1016 10.1016 NUM cana-3280 188 11 / / SYM cana-3280 188 12 j.array.2022.100190 j.array.2022.100190 NOUN cana-3280 188 13 . . PUNCT cana-3280 189 1 [ [ X cana-3280 189 2 2 2 NUM cana-3280 189 3 ] ] PUNCT cana-3280 189 4 shruthi shruthi PROPN cana-3280 189 5 , , PUNCT cana-3280 189 6 p. p. PROPN cana-3280 189 7 pattan pattan PROPN cana-3280 189 8 , , PUNCT cana-3280 189 9 and and CCONJ cana-3280 189 10 s. s. PROPN cana-3280 189 11 arjunagi arjunagi PROPN cana-3280 189 12 , , PUNCT cana-3280 189 13 β€œ " PUNCT cana-3280 189 14 a a DET cana-3280 189 15 human human ADJ cana-3280 189 16 behavior behavior NOUN cana-3280 189 17 analysis analysis NOUN cana-3280 189 18 model model NOUN cana-3280 189 19 to to PART cana-3280 189 20 track track VERB cana-3280 189 21 object object NOUN cana-3280 189 22 behavior behavior NOUN cana-3280 189 23 in in ADP cana-3280 189 24 surveillance surveillance NOUN cana-3280 189 25 videos video NOUN cana-3280 189 26 , , PUNCT cana-3280 189 27 ” " PUNCT cana-3280 189 28 meas mea NOUN cana-3280 189 29 . . PUNCT cana-3280 190 1 sensors sensor NOUN cana-3280 190 2 , , PUNCT cana-3280 190 3 vol vol NOUN cana-3280 190 4 . . PROPN cana-3280 190 5 24 24 NUM cana-3280 190 6 , , PUNCT cana-3280 190 7 no no INTJ cana-3280 190 8 . . PUNCT cana-3280 191 1 august august PROPN cana-3280 191 2 , , PUNCT cana-3280 191 3 p. p. NOUN cana-3280 191 4 100454 100454 NUM cana-3280 191 5 , , PUNCT cana-3280 191 6 2022 2022 NUM cana-3280 191 7 , , PUNCT cana-3280 191 8 doi doi NOUN cana-3280 191 9 : : PUNCT cana-3280 191 10 10.1016 10.1016 NUM cana-3280 191 11 / / SYM cana-3280 191 12 j.measen.2022.100454 j.measen.2022.100454 NOUN cana-3280 191 13 . . PUNCT cana-3280 192 1 [ [ X cana-3280 192 2 3 3 X cana-3280 192 3 ] ] PUNCT cana-3280 192 4 j. j. PROPN cana-3280 192 5 yang yang PROPN cana-3280 192 6 , , PUNCT cana-3280 192 7 y. y. PROPN cana-3280 192 8 xu xu PROPN cana-3280 192 9 , , PUNCT cana-3280 192 10 h. h. PROPN cana-3280 192 11 cao cao PROPN cana-3280 192 12 , , PUNCT cana-3280 192 13 h. h. PROPN cana-3280 192 14 zou zou PROPN cana-3280 192 15 , , PUNCT cana-3280 192 16 and and CCONJ cana-3280 192 17 l. l. PROPN cana-3280 192 18 xie xie PROPN cana-3280 192 19 , , PUNCT cana-3280 192 20 β€œ " PUNCT cana-3280 192 21 deep deep ADJ cana-3280 192 22 learning learning NOUN cana-3280 192 23 and and CCONJ cana-3280 192 24 transfer transfer VERB cana-3280 192 25 learning learning NOUN cana-3280 192 26 for for ADP cana-3280 192 27 device device NOUN cana-3280 192 28 - - PUNCT cana-3280 192 29 free free ADJ cana-3280 192 30 human human ADJ cana-3280 192 31 activity activity NOUN cana-3280 192 32 recognition recognition NOUN cana-3280 192 33 : : PUNCT cana-3280 192 34 a a DET cana-3280 192 35 survey survey NOUN cana-3280 192 36 , , PUNCT cana-3280 192 37 ” " PUNCT cana-3280 192 38 j. j. PROPN cana-3280 192 39 autom autom PROPN cana-3280 192 40 . . PUNCT cana-3280 193 1 intell intell PROPN cana-3280 193 2 . . PUNCT cana-3280 193 3 , , PUNCT cana-3280 193 4 vol vol NOUN cana-3280 193 5 . . PROPN cana-3280 194 1 1 1 NUM cana-3280 194 2 , , PUNCT cana-3280 194 3 no no INTJ cana-3280 194 4 . . NOUN cana-3280 194 5 1 1 NUM cana-3280 194 6 , , PUNCT cana-3280 194 7 p. p. NOUN cana-3280 194 8 100007 100007 NUM cana-3280 194 9 , , PUNCT cana-3280 194 10 2022 2022 NUM cana-3280 194 11 , , PUNCT cana-3280 194 12 doi doi NOUN cana-3280 194 13 : : PUNCT cana-3280 194 14 10.1016 10.1016 NUM cana-3280 194 15 / / SYM cana-3280 194 16 j.jai.2022.100007 j.jai.2022.100007 ADJ cana-3280 194 17 . . PUNCT cana-3280 195 1 [ [ X cana-3280 195 2 4 4 X cana-3280 195 3 ] ] PUNCT cana-3280 195 4 l. l. PROPN cana-3280 195 5 zhu zhu PROPN cana-3280 195 6 and and CCONJ cana-3280 195 7 l. l. PROPN cana-3280 195 8 liu liu PROPN cana-3280 195 9 , , PUNCT cana-3280 195 10 β€œ " PUNCT cana-3280 195 11 3d 3d NUM cana-3280 195 12 human human ADJ cana-3280 195 13 motion motion NOUN cana-3280 195 14 posture posture NOUN cana-3280 195 15 tracking tracking NOUN cana-3280 195 16 method method NOUN cana-3280 195 17 using use VERB cana-3280 195 18 multilabel multilabel NOUN cana-3280 195 19 transfer transfer NOUN cana-3280 195 20 learning learning NOUN cana-3280 195 21 , , PUNCT cana-3280 195 22 ” " PUNCT cana-3280 195 23 mob mob NOUN cana-3280 195 24 . . PUNCT cana-3280 195 25 inf inf PROPN cana-3280 195 26 . . PUNCT cana-3280 195 27 syst syst PROPN cana-3280 195 28 . . PUNCT cana-3280 195 29 , , PUNCT cana-3280 195 30 vol vol NOUN cana-3280 195 31 . . NOUN cana-3280 195 32 2022 2022 NUM cana-3280 195 33 , , PUNCT cana-3280 195 34 2022 2022 NUM cana-3280 195 35 , , PUNCT cana-3280 195 36 doi doi NOUN cana-3280 195 37 : : PUNCT cana-3280 195 38 10.1155/2022/2211866 10.1155/2022/2211866 NUM cana-3280 195 39 . . PUNCT cana-3280 196 1 [ [ X cana-3280 196 2 5 5 NUM cana-3280 196 3 ] ] PUNCT cana-3280 196 4 β€œ " PUNCT cana-3280 196 5 convolutional convolutional ADJ cana-3280 196 6 neural neural ADJ cana-3280 196 7 network network NOUN cana-3280 196 8 ( ( PUNCT cana-3280 196 9 cnn cnn PROPN cana-3280 196 10 ) ) PUNCT cana-3280 196 11 in in ADP cana-3280 196 12 machine machine NOUN cana-3280 196 13 learning learn VERB cana-3280 196 14 geeksforgeeks geeksforgeek NOUN cana-3280 196 15 . . PUNCT cana-3280 196 16 ” " PUNCT cana-3280 196 17 https://www.geeksforgeeks.org/convolutional-neural-network-cnn-in-machine-learning/ https://www.geeksforgeeks.org/convolutional-neural-network-cnn-in-machine-learning/ VERB cana-3280 196 18 ( ( PUNCT cana-3280 196 19 accessed access VERB cana-3280 196 20 apr apr NOUN cana-3280 196 21 . . PROPN cana-3280 196 22 25 25 NUM cana-3280 196 23 , , PUNCT cana-3280 196 24 2023 2023 NUM cana-3280 196 25 ) ) PUNCT cana-3280 196 26 . . PUNCT cana-3280 197 1 [ [ X cana-3280 197 2 6 6 NUM cana-3280 197 3 ] ] PUNCT cana-3280 197 4 β€œ " PUNCT cana-3280 197 5 an an DET cana-3280 197 6 overview overview NOUN cana-3280 197 7 of of ADP cana-3280 197 8 deep deep ADJ cana-3280 197 9 belief belief NOUN cana-3280 197 10 network network NOUN cana-3280 197 11 ( ( PUNCT cana-3280 197 12 dbn dbn PROPN cana-3280 197 13 ) ) PUNCT cana-3280 197 14 in in ADP cana-3280 197 15 deep deep ADJ cana-3280 197 16 learning learning NOUN cana-3280 197 17 . . PUNCT cana-3280 197 18 ” " PUNCT cana-3280 197 19 https://www.analyticsvidhya.com/blog/2022/03/an-overview-of-deep-belief-network-dbn-in-deep-learning/ https://www.analyticsvidhya.com/blog/2022/03/an-overview-of-deep-belief-network-dbn-in-deep-learning/ VERB cana-3280 197 20 ( ( PUNCT cana-3280 197 21 accessed access VERB cana-3280 197 22 apr apr NOUN cana-3280 197 23 . . PROPN cana-3280 197 24 25 25 NUM cana-3280 197 25 , , PUNCT cana-3280 197 26 2023 2023 NUM cana-3280 197 27 ) ) PUNCT cana-3280 197 28 . . PUNCT cana-3280 198 1 [ [ X cana-3280 198 2 7 7 X cana-3280 198 3 ] ] PUNCT cana-3280 198 4 a. a. NOUN cana-3280 198 5 hussain hussain PROPN cana-3280 198 6 , , PUNCT cana-3280 198 7 t. t. PROPN cana-3280 198 8 hussain hussain PROPN cana-3280 198 9 , , PUNCT cana-3280 198 10 w. w. PROPN cana-3280 198 11 ullah ullah PROPN cana-3280 198 12 , , PUNCT cana-3280 198 13 and and CCONJ cana-3280 198 14 s. s. PROPN cana-3280 198 15 w. w. PROPN cana-3280 198 16 baik baik PROPN cana-3280 198 17 , , PUNCT cana-3280 198 18 β€œ " PUNCT cana-3280 198 19 vision vision NOUN cana-3280 198 20 transformer transformer NOUN cana-3280 198 21 and and CCONJ cana-3280 198 22 deep deep ADJ cana-3280 198 23 sequence sequence NOUN cana-3280 198 24 learning learn VERB cana-3280 198 25 for for ADP cana-3280 198 26 human human ADJ cana-3280 198 27 activity activity NOUN cana-3280 198 28 recognition recognition NOUN cana-3280 198 29 in in ADP cana-3280 198 30 surveillance surveillance NOUN cana-3280 198 31 videos video NOUN cana-3280 198 32 , , PUNCT cana-3280 198 33 ” " PUNCT cana-3280 198 34 comput comput NOUN cana-3280 198 35 . . PUNCT cana-3280 199 1 intell intell PROPN cana-3280 199 2 . . PUNCT cana-3280 200 1 neurosci neurosci PROPN cana-3280 200 2 . . PUNCT cana-3280 200 3 , , PUNCT cana-3280 200 4 vol vol NOUN cana-3280 200 5 . . PROPN cana-3280 200 6 2022 2022 NUM cana-3280 200 7 , , PUNCT cana-3280 200 8 no no INTJ cana-3280 200 9 . . NOUN cana-3280 200 10 1 1 NUM cana-3280 200 11 , , PUNCT cana-3280 200 12 2022 2022 NUM cana-3280 200 13 , , PUNCT cana-3280 200 14 doi doi NOUN cana-3280 200 15 : : PUNCT cana-3280 200 16 10.1155/2022/3454167 10.1155/2022/3454167 NUM cana-3280 200 17 . . PUNCT cana-3280 201 1 [ [ X cana-3280 201 2 8 8 NUM cana-3280 201 3 ] ] X cana-3280 201 4 d. d. PROPN cana-3280 201 5 sun sun PROPN cana-3280 201 6 , , PUNCT cana-3280 201 7 j. j. PROPN cana-3280 201 8 zhang zhang PROPN cana-3280 201 9 , , PUNCT cana-3280 201 10 s. s. PROPN cana-3280 201 11 zhang zhang PROPN cana-3280 201 12 , , PUNCT cana-3280 201 13 x. x. PROPN cana-3280 201 14 li li PROPN cana-3280 201 15 , , PUNCT cana-3280 201 16 and and CCONJ cana-3280 201 17 h. h. PROPN cana-3280 201 18 wang wang PROPN cana-3280 201 19 , , PUNCT cana-3280 201 20 β€œ " PUNCT cana-3280 201 21 human human ADJ cana-3280 201 22 health health NOUN cana-3280 201 23 activity activity NOUN cana-3280 201 24 recognition recognition NOUN cana-3280 201 25 algorithm algorithm NOUN cana-3280 201 26 in in ADP cana-3280 201 27 wireless wireless ADJ cana-3280 201 28 sensor sensor NOUN cana-3280 201 29 networks network NOUN cana-3280 201 30 based base VERB cana-3280 201 31 on on ADP cana-3280 201 32 metric metric ADJ cana-3280 201 33 learning learning NOUN cana-3280 201 34 , , PUNCT cana-3280 201 35 ” " PUNCT cana-3280 201 36 comput comput NOUN cana-3280 201 37 . . PUNCT cana-3280 202 1 intell intell PROPN cana-3280 202 2 . . PUNCT cana-3280 203 1 neurosci neurosci PROPN cana-3280 203 2 . . PUNCT cana-3280 203 3 , , PUNCT cana-3280 203 4 vol vol NOUN cana-3280 203 5 . . NOUN cana-3280 203 6 2022 2022 NUM cana-3280 203 7 , , PUNCT cana-3280 203 8 2022 2022 NUM cana-3280 203 9 , , PUNCT cana-3280 203 10 doi doi NOUN cana-3280 203 11 : : PUNCT cana-3280 203 12 10.1155/2022/4204644 10.1155/2022/4204644 NUM cana-3280 203 13 . . PUNCT cana-3280 204 1 [ [ X cana-3280 204 2 9 9 NUM cana-3280 204 3 ] ] PUNCT cana-3280 204 4 l. l. PROPN cana-3280 204 5 qiao qiao PROPN cana-3280 204 6 and and CCONJ cana-3280 204 7 q. q. PROPN cana-3280 204 8 h. h. PROPN cana-3280 204 9 shen shen PROPN cana-3280 204 10 , , PUNCT cana-3280 204 11 β€œ " PUNCT cana-3280 204 12 human human ADJ cana-3280 204 13 action action NOUN cana-3280 204 14 recognition recognition NOUN cana-3280 204 15 technology technology NOUN cana-3280 204 16 in in ADP cana-3280 204 17 dance dance NOUN cana-3280 204 18 video video NOUN cana-3280 204 19 image image NOUN cana-3280 204 20 , , PUNCT cana-3280 204 21 ” " PUNCT cana-3280 204 22 sci sci PROPN cana-3280 204 23 . . PROPN cana-3280 204 24 program program PROPN cana-3280 204 25 . . PUNCT cana-3280 204 26 , , PUNCT cana-3280 204 27 vol vol NOUN cana-3280 204 28 . . NOUN cana-3280 204 29 2021 2021 NUM cana-3280 204 30 , , PUNCT cana-3280 204 31 2021 2021 NUM cana-3280 204 32 , , PUNCT cana-3280 204 33 doi doi NOUN cana-3280 204 34 : : PUNCT cana-3280 204 35 10.1155/2021/6144762 10.1155/2021/6144762 NUM cana-3280 204 36 . . PUNCT cana-3280 205 1 [ [ X cana-3280 205 2 10 10 NUM cana-3280 205 3 ] ] X cana-3280 205 4 a. a. NOUN cana-3280 205 5 mihoub mihoub PROPN cana-3280 205 6 , , PUNCT cana-3280 205 7 β€œ " PUNCT cana-3280 205 8 a a DET cana-3280 205 9 deep deep ADJ cana-3280 205 10 learning learning NOUN cana-3280 205 11 - - PUNCT cana-3280 205 12 based base VERB cana-3280 205 13 framework framework NOUN cana-3280 205 14 for for ADP cana-3280 205 15 human human ADJ cana-3280 205 16 activity activity NOUN cana-3280 205 17 recognition recognition NOUN cana-3280 205 18 in in ADP cana-3280 205 19 smart smart ADJ cana-3280 205 20 homes home NOUN cana-3280 205 21 , , PUNCT cana-3280 205 22 ” " PUNCT cana-3280 205 23 mob mob NOUN cana-3280 205 24 . . PUNCT cana-3280 205 25 inf inf PROPN cana-3280 205 26 . . PUNCT cana-3280 205 27 syst syst PROPN cana-3280 205 28 . . PUNCT cana-3280 205 29 , , PUNCT cana-3280 205 30 vol vol NOUN cana-3280 205 31 . . NOUN cana-3280 205 32 2021 2021 NUM cana-3280 205 33 , , PUNCT cana-3280 205 34 2021 2021 NUM cana-3280 205 35 , , PUNCT cana-3280 205 36 doi doi NOUN cana-3280 205 37 : : PUNCT cana-3280 205 38 10.1155/2021/6961343 10.1155/2021/6961343 NUM cana-3280 205 39 . . PUNCT cana-3280 206 1 [ [ X cana-3280 206 2 11 11 NUM cana-3280 206 3 ] ] PUNCT cana-3280 206 4 t. t. PROPN cana-3280 206 5 george george PROPN cana-3280 206 6 karimpanal karimpanal PROPN cana-3280 206 7 and and CCONJ cana-3280 206 8 r. r. PROPN cana-3280 206 9 bouffanais bouffanais PROPN cana-3280 206 10 , , PUNCT cana-3280 206 11 β€œ " PUNCT cana-3280 206 12 self self NOUN cana-3280 206 13 - - PUNCT cana-3280 206 14 organizing organize VERB cana-3280 206 15 maps map NOUN cana-3280 206 16 for for ADP cana-3280 206 17 storage storage NOUN cana-3280 206 18 and and CCONJ cana-3280 206 19 transfer transfer NOUN cana-3280 206 20 of of ADP cana-3280 206 21 knowledge knowledge NOUN cana-3280 206 22 in in ADP cana-3280 206 23 reinforcement reinforcement NOUN cana-3280 206 24 learning learning NOUN cana-3280 206 25 , , PUNCT cana-3280 206 26 ” " PUNCT cana-3280 206 27 adapt adapt VERB cana-3280 206 28 . . PUNCT cana-3280 206 29 behav behav PROPN cana-3280 206 30 . . PUNCT cana-3280 206 31 , , PUNCT cana-3280 206 32 vol vol NOUN cana-3280 206 33 . . PROPN cana-3280 207 1 27 27 NUM cana-3280 207 2 , , PUNCT cana-3280 207 3 no no INTJ cana-3280 207 4 . . NOUN cana-3280 207 5 2 2 NUM cana-3280 207 6 , , PUNCT cana-3280 207 7 pp pp ADJ cana-3280 207 8 . . PUNCT cana-3280 208 1 111–126 111–126 NUM cana-3280 208 2 , , PUNCT cana-3280 208 3 apr apr PROPN cana-3280 208 4 . . PROPN cana-3280 208 5 2019 2019 NUM cana-3280 208 6 , , PUNCT cana-3280 208 7 doi doi NOUN cana-3280 208 8 : : PUNCT cana-3280 208 9 10.1177/1059712318818568 10.1177/1059712318818568 NUM cana-3280 208 10 . . PUNCT cana-3280 209 1 [ [ X cana-3280 209 2 12 12 NUM cana-3280 209 3 ] ] X cana-3280 209 4 s. s. PROPN cana-3280 209 5 li li PROPN cana-3280 209 6 , , PUNCT cana-3280 209 7 j. j. PROPN cana-3280 209 8 fan fan PROPN cana-3280 209 9 , , PUNCT cana-3280 209 10 p. p. PROPN cana-3280 209 11 zheng zheng PROPN cana-3280 209 12 , , PUNCT cana-3280 209 13 and and CCONJ cana-3280 209 14 l. l. PROPN cana-3280 209 15 wang wang PROPN cana-3280 209 16 , , PUNCT cana-3280 209 17 β€œ " PUNCT cana-3280 209 18 transfer transfer VERB cana-3280 209 19 learning learning NOUN cana-3280 209 20 - - PUNCT cana-3280 209 21 enabled enable VERB cana-3280 209 22 action action NOUN cana-3280 209 23 recognition recognition NOUN cana-3280 209 24 for for ADP cana-3280 209 25 human human ADJ cana-3280 209 26 - - PUNCT cana-3280 209 27 robot robot NOUN cana-3280 209 28 collaborative collaborative ADJ cana-3280 209 29 assembly assembly NOUN cana-3280 209 30 , , PUNCT cana-3280 209 31 ” " PUNCT cana-3280 209 32 procedia procedia NOUN cana-3280 209 33 cirp cirp NOUN cana-3280 209 34 , , PUNCT cana-3280 209 35 vol vol NOUN cana-3280 209 36 . . PROPN cana-3280 210 1 104 104 NUM cana-3280 210 2 , , PUNCT cana-3280 210 3 no no INTJ cana-3280 210 4 . . PUNCT cana-3280 211 1 march march PROPN cana-3280 211 2 , , PUNCT cana-3280 211 3 pp pp ADV cana-3280 211 4 . . PUNCT cana-3280 212 1 1795–1800 1795–1800 NUM cana-3280 212 2 , , PUNCT cana-3280 212 3 2021 2021 NUM cana-3280 212 4 , , PUNCT cana-3280 212 5 doi doi NOUN cana-3280 212 6 : : PUNCT cana-3280 212 7 10.1016 10.1016 NUM cana-3280 212 8 / / SYM cana-3280 212 9 j.procir.2021.11.303 j.procir.2021.11.303 X cana-3280 212 10 . . PUNCT cana-3280 213 1 [ [ X cana-3280 213 2 13 13 NUM cana-3280 213 3 ] ] X cana-3280 213 4 w. w. PROPN cana-3280 213 5 lao lao PROPN cana-3280 213 6 , , PUNCT cana-3280 213 7 j. j. PROPN cana-3280 213 8 han han PROPN cana-3280 213 9 , , PUNCT cana-3280 213 10 and and CCONJ cana-3280 213 11 p. p. PROPN cana-3280 213 12 h. h. PROPN cana-3280 213 13 n. n. PROPN cana-3280 213 14 de de PROPN cana-3280 213 15 with with ADP cana-3280 213 16 , , PUNCT cana-3280 213 17 β€œ " PUNCT cana-3280 213 18 flexible flexible ADJ cana-3280 213 19 human human ADJ cana-3280 213 20 behavior behavior NOUN cana-3280 213 21 analysis analysis NOUN cana-3280 213 22 framework framework NOUN cana-3280 213 23 for for ADP cana-3280 213 24 video video NOUN cana-3280 213 25 surveillance surveillance NOUN cana-3280 213 26 applications application NOUN cana-3280 213 27 , , PUNCT cana-3280 213 28 ” " PUNCT cana-3280 213 29 int int NOUN cana-3280 213 30 . . PUNCT cana-3280 214 1 j. j. PROPN cana-3280 214 2 digit digit PROPN cana-3280 214 3 . . PUNCT cana-3280 214 4 multimed multimed PROPN cana-3280 214 5 . . PUNCT cana-3280 215 1 broadcast broadcast PROPN cana-3280 215 2 . . PUNCT cana-3280 216 1 , , PUNCT cana-3280 216 2 vol vol NOUN cana-3280 216 3 . . PROPN cana-3280 217 1 2010 2010 NUM cana-3280 217 2 , , PUNCT cana-3280 217 3 2010 2010 NUM cana-3280 217 4 , , PUNCT cana-3280 217 5 doi doi NOUN cana-3280 217 6 : : PUNCT cana-3280 217 7 10.1155/2010/920121 10.1155/2010/920121 NUM cana-3280 217 8 . . PUNCT cana-3280 218 1 [ [ X cana-3280 218 2 14 14 NUM cana-3280 218 3 ] ] X cana-3280 218 4 j. j. PROPN cana-3280 218 5 sun sun PROPN cana-3280 218 6 , , PUNCT cana-3280 218 7 y. y. PROPN cana-3280 218 8 fu fu PROPN cana-3280 218 9 , , PUNCT cana-3280 218 10 s. s. PROPN cana-3280 218 11 li li PROPN cana-3280 218 12 , , PUNCT cana-3280 218 13 j. j. PROPN cana-3280 218 14 he he PROPN cana-3280 218 15 , , PUNCT cana-3280 218 16 c. c. PROPN cana-3280 218 17 xu xu PROPN cana-3280 218 18 , , PUNCT cana-3280 218 19 and and CCONJ cana-3280 218 20 l. l. PROPN cana-3280 218 21 tan tan PROPN cana-3280 218 22 , , PUNCT cana-3280 218 23 β€œ " PUNCT cana-3280 218 24 sequential sequential ADJ cana-3280 218 25 human human ADJ cana-3280 218 26 activity activity NOUN cana-3280 218 27 recognition recognition NOUN cana-3280 218 28 based base VERB cana-3280 218 29 on on ADP cana-3280 218 30 deep deep ADJ cana-3280 218 31 convolutional convolutional ADJ cana-3280 218 32 network network NOUN cana-3280 218 33 and and CCONJ cana-3280 218 34 extreme extreme ADJ cana-3280 218 35 learning learning NOUN cana-3280 218 36 machine machine NOUN cana-3280 218 37 using use VERB cana-3280 218 38 wearable wearable ADJ cana-3280 218 39 sensors sensor NOUN cana-3280 218 40 , , PUNCT cana-3280 218 41 ” " PUNCT cana-3280 218 42 j. j. PROPN cana-3280 218 43 sensors sensors PROPN cana-3280 218 44 , , PUNCT cana-3280 218 45 vol vol NOUN cana-3280 218 46 . . PROPN cana-3280 218 47 2018 2018 NUM cana-3280 218 48 , , PUNCT cana-3280 218 49 no no INTJ cana-3280 218 50 . . NOUN cana-3280 218 51 1 1 NUM cana-3280 218 52 , , PUNCT cana-3280 218 53 2018 2018 NUM cana-3280 218 54 , , PUNCT cana-3280 218 55 doi doi NOUN cana-3280 218 56 : : PUNCT cana-3280 218 57 10.1155/2018/8580959 10.1155/2018/8580959 NUM cana-3280 218 58 . . PUNCT cana-3280 219 1 [ [ X cana-3280 219 2 15 15 NUM cana-3280 219 3 ] ] X cana-3280 219 4 y. y. PROPN cana-3280 219 5 y. y. PROPN cana-3280 219 6 zhu zhu PROPN cana-3280 219 7 , , PUNCT cana-3280 219 8 y. y. PROPN cana-3280 219 9 y. y. PROPN cana-3280 219 10 zhu zhu PROPN cana-3280 219 11 , , PUNCT cana-3280 219 12 z. z. PROPN cana-3280 219 13 k. k. PROPN cana-3280 219 14 wen wen PROPN cana-3280 219 15 , , PUNCT cana-3280 219 16 w. w. PROPN cana-3280 219 17 s. s. PROPN cana-3280 219 18 chen chen PROPN cana-3280 219 19 , , PUNCT cana-3280 219 20 and and CCONJ cana-3280 219 21 q. q. PROPN cana-3280 219 22 huang huang PROPN cana-3280 219 23 , , PUNCT cana-3280 219 24 β€œ " PUNCT cana-3280 219 25 detection detection NOUN cana-3280 219 26 and and CCONJ cana-3280 219 27 recognition recognition NOUN cana-3280 219 28 of of ADP cana-3280 219 29 abnormal abnormal ADJ cana-3280 219 30 running running NOUN cana-3280 219 31 behavior behavior NOUN cana-3280 219 32 in in ADP cana-3280 219 33 surveillance surveillance NOUN cana-3280 219 34 video video NOUN cana-3280 219 35 , , PUNCT cana-3280 219 36 ” " PUNCT cana-3280 219 37 math math NOUN cana-3280 219 38 . . PUNCT cana-3280 220 1 probl probl PROPN cana-3280 220 2 . . PUNCT cana-3280 221 1 eng eng PROPN cana-3280 221 2 . . PROPN cana-3280 221 3 , , PUNCT cana-3280 221 4 vol vol NOUN cana-3280 221 5 . . PROPN cana-3280 221 6 2012 2012 NUM cana-3280 221 7 , , PUNCT cana-3280 221 8 2012 2012 NUM cana-3280 221 9 , , PUNCT cana-3280 221 10 doi doi NOUN cana-3280 221 11 : : PUNCT cana-3280 221 12 10.1155/2012/296407 10.1155/2012/296407 NUM cana-3280 221 13 . . PUNCT cana-3280 222 1 [ [ X cana-3280 222 2 16 16 NUM cana-3280 222 3 ] ] PUNCT cana-3280 222 4 β€œ " PUNCT cana-3280 222 5 brief brief ADJ cana-3280 222 6 review review NOUN cana-3280 222 7 β€” β€” PUNCT cana-3280 222 8 lrcn lrcn PROPN cana-3280 222 9 : : PUNCT cana-3280 222 10 long long ADJ cana-3280 222 11 - - PUNCT cana-3280 222 12 term term NOUN cana-3280 222 13 recurrent recurrent ADJ cana-3280 222 14 convolutional convolutional ADJ cana-3280 222 15 networks network NOUN cana-3280 222 16 for for ADP cana-3280 222 17 visual visual ADJ cana-3280 222 18 recognition recognition NOUN cana-3280 222 19 and and CCONJ cana-3280 222 20 description description NOUN cana-3280 222 21 | | ADV cana-3280 222 22 by by ADP cana-3280 222 23 sik sik ADJ cana-3280 222 24 - - PUNCT cana-3280 222 25 ho ho PROPN cana-3280 222 26 tsang tsang PROPN cana-3280 222 27 | | PROPN cana-3280 222 28 medium medium NOUN cana-3280 222 29 . . PUNCT cana-3280 222 30 ” " PUNCT cana-3280 223 1 https://sh-tsang.medium.com/brief-review-lrcn-long-term-recurrent-convolutionalcommunications https://sh-tsang.medium.com/brief-review-lrcn-long-term-recurrent-convolutionalcommunication NOUN cana-3280 223 2 on on ADP cana-3280 223 3 applied apply VERB cana-3280 223 4 nonlinear nonlinear ADJ cana-3280 223 5 analysis analysis NOUN cana-3280 223 6 issn issn NOUN cana-3280 223 7 : : PUNCT cana-3280 223 8 1074 1074 NUM cana-3280 223 9 - - PUNCT cana-3280 223 10 133x 133x NUM cana-3280 223 11 vol vol NOUN cana-3280 223 12 32 32 NUM cana-3280 223 13 no no NOUN cana-3280 223 14 . . PUNCT cana-3280 224 1 6s 6s NUM cana-3280 224 2 ( ( PUNCT cana-3280 224 3 2025 2025 NUM cana-3280 224 4 ) ) PUNCT cana-3280 224 5 133 133 NUM cana-3280 224 6 https://internationalpubls.com https://internationalpubls.com X cana-3280 224 7 networks network NOUN cana-3280 224 8 - - PUNCT cana-3280 224 9 for for ADP cana-3280 224 10 - - PUNCT cana-3280 224 11 visual visual ADJ cana-3280 224 12 - - PUNCT cana-3280 224 13 recognition recognition NOUN cana-3280 224 14 - - PUNCT cana-3280 224 15 and-9542bc7e8a79 and-9542bc7e8a79 NOUN cana-3280 224 16 ( ( PUNCT cana-3280 224 17 accessed access VERB cana-3280 224 18 apr apr NOUN cana-3280 224 19 . . PROPN cana-3280 224 20 25 25 NUM cana-3280 224 21 , , PUNCT cana-3280 224 22 2023 2023 NUM cana-3280 224 23 ) ) PUNCT cana-3280 224 24 . . PUNCT cana-3280 225 1 [ [ X cana-3280 225 2 17 17 NUM cana-3280 225 3 ] ] PUNCT cana-3280 225 4 s. s. PROPN cana-3280 225 5 m. m. PROPN cana-3280 225 6 hejazi hejazi PROPN cana-3280 225 7 and and CCONJ cana-3280 225 8 c. c. PROPN cana-3280 225 9 abhayaratne abhayaratne PROPN cana-3280 225 10 , , PUNCT cana-3280 225 11 β€œ " PUNCT cana-3280 225 12 handcrafted handcraft VERB cana-3280 225 13 localized localize VERB cana-3280 225 14 phase phase NOUN cana-3280 225 15 features feature NOUN cana-3280 225 16 for for ADP cana-3280 225 17 human human ADJ cana-3280 225 18 action action NOUN cana-3280 225 19 recognition recognition NOUN cana-3280 225 20 , , PUNCT cana-3280 225 21 ” " PUNCT cana-3280 225 22 image image NOUN cana-3280 225 23 vis vis X cana-3280 225 24 . . PUNCT cana-3280 225 25 comput comput NOUN cana-3280 225 26 . . PUNCT cana-3280 225 27 , , PUNCT cana-3280 225 28 vol vol NOUN cana-3280 225 29 . . NOUN cana-3280 225 30 123 123 NUM cana-3280 225 31 , , PUNCT cana-3280 225 32 p. p. NOUN cana-3280 225 33 104465 104465 NUM cana-3280 225 34 , , PUNCT cana-3280 225 35 2022 2022 NUM cana-3280 225 36 , , PUNCT cana-3280 225 37 doi doi NOUN cana-3280 225 38 : : PUNCT cana-3280 225 39 10.1016 10.1016 NUM cana-3280 225 40 / / SYM cana-3280 225 41 j.imavis.2022.104465 j.imavis.2022.104465 NOUN cana-3280 225 42 . . PUNCT cana-3280 226 1 [ [ X cana-3280 226 2 18 18 NUM cana-3280 226 3 ] ] PUNCT cana-3280 226 4 x. x. NOUN cana-3280 226 5 cui cui PROPN cana-3280 226 6 and and CCONJ cana-3280 226 7 r. r. PROPN cana-3280 226 8 hu hu PROPN cana-3280 226 9 , , PUNCT cana-3280 226 10 β€œ " PUNCT cana-3280 226 11 application application NOUN cana-3280 226 12 of of ADP cana-3280 226 13 intelligent intelligent ADJ cana-3280 226 14 edge edge NOUN cana-3280 226 15 computing compute VERB cana-3280 226 16 technology technology NOUN cana-3280 226 17 for for ADP cana-3280 226 18 video video NOUN cana-3280 226 19 surveillance surveillance NOUN cana-3280 226 20 in in ADP cana-3280 226 21 human human ADJ cana-3280 226 22 movement movement NOUN cana-3280 226 23 recognition recognition PROPN cana-3280 226 24 and and CCONJ cana-3280 226 25 taekwondo taekwondo NOUN cana-3280 226 26 training training NOUN cana-3280 226 27 , , PUNCT cana-3280 226 28 ” " PUNCT cana-3280 226 29 alexandria alexandria PROPN cana-3280 226 30 eng eng PROPN cana-3280 226 31 . . PUNCT cana-3280 227 1 j. j. PROPN cana-3280 227 2 , , PUNCT cana-3280 227 3 vol vol NOUN cana-3280 227 4 . . PROPN cana-3280 227 5 61 61 NUM cana-3280 227 6 , , PUNCT cana-3280 227 7 no no INTJ cana-3280 227 8 . . NOUN cana-3280 227 9 4 4 NUM cana-3280 227 10 , , PUNCT cana-3280 227 11 pp pp ADJ cana-3280 227 12 . . PUNCT cana-3280 227 13 2899–2908 2899–2908 NUM cana-3280 227 14 , , PUNCT cana-3280 227 15 2022 2022 NUM cana-3280 227 16 , , PUNCT cana-3280 227 17 doi doi NOUN cana-3280 227 18 : : PUNCT cana-3280 227 19 10.1016 10.1016 NUM cana-3280 227 20 / / SYM cana-3280 227 21 j.aej.2021.08.020 j.aej.2021.08.020 NOUN cana-3280 227 22 . . PUNCT cana-3280 228 1 [ [ X cana-3280 228 2 19 19 NUM cana-3280 228 3 ] ] PUNCT cana-3280 228 4 r. r. PROPN cana-3280 228 5 mar mar PROPN cana-3280 228 6 - - PUNCT cana-3280 228 7 cupido cupido PROPN cana-3280 228 8 , , PUNCT cana-3280 228 9 v. v. ADP cana-3280 228 10 garcΓ­a garcΓ­a ADJ cana-3280 228 11 , , PUNCT cana-3280 228 12 g. g. PROPN cana-3280 228 13 rivera rivera PROPN cana-3280 228 14 , , PUNCT cana-3280 228 15 and and CCONJ cana-3280 228 16 j. j. PROPN cana-3280 228 17 s. s. PROPN cana-3280 228 18 sΓ‘nchez sΓ‘nchez PROPN cana-3280 228 19 , , PUNCT cana-3280 228 20 β€œ " PUNCT cana-3280 228 21 deep deep ADJ cana-3280 228 22 transfer transfer NOUN cana-3280 228 23 learning learning NOUN cana-3280 228 24 for for ADP cana-3280 228 25 the the DET cana-3280 228 26 recognition recognition NOUN cana-3280 228 27 of of ADP cana-3280 228 28 types type NOUN cana-3280 228 29 of of ADP cana-3280 228 30 face face NOUN cana-3280 228 31 masks mask NOUN cana-3280 228 32 as as ADP cana-3280 228 33 a a DET cana-3280 228 34 core core NOUN cana-3280 228 35 measure measure NOUN cana-3280 228 36 to to PART cana-3280 228 37 prevent prevent VERB cana-3280 228 38 the the DET cana-3280 228 39 transmission transmission NOUN cana-3280 228 40 of of ADP cana-3280 228 41 covid-19 covid-19 PROPN cana-3280 228 42 , , PUNCT cana-3280 228 43 ” " PUNCT cana-3280 228 44 appl appl NOUN cana-3280 228 45 . . PUNCT cana-3280 228 46 soft soft ADJ cana-3280 228 47 comput comput NOUN cana-3280 228 48 . . PUNCT cana-3280 228 49 , , PUNCT cana-3280 228 50 vol vol NOUN cana-3280 228 51 . . PROPN cana-3280 228 52 125 125 NUM cana-3280 228 53 , , PUNCT cana-3280 228 54 p. p. NOUN cana-3280 228 55 109207 109207 NUM cana-3280 228 56 , , PUNCT cana-3280 228 57 2022 2022 NUM cana-3280 228 58 , , PUNCT cana-3280 228 59 doi doi NOUN cana-3280 228 60 : : PUNCT cana-3280 228 61 10.1016 10.1016 NUM cana-3280 228 62 / / SYM cana-3280 228 63 j.asoc.2022.109207 j.asoc.2022.109207 NOUN cana-3280 228 64 . . PUNCT cana-3280 229 1 [ [ X cana-3280 229 2 20 20 NUM cana-3280 229 3 ] ] PUNCT cana-3280 229 4 v. v. CCONJ cana-3280 229 5 sarveshwaran sarveshwaran NOUN cana-3280 229 6 , , PUNCT cana-3280 229 7 i. i. PROPN cana-3280 229 8 t. t. PROPN cana-3280 229 9 joseph joseph PROPN cana-3280 229 10 , , PUNCT cana-3280 229 11 m. m. NOUN cana-3280 229 12 maravarman maravarman PROPN cana-3280 229 13 , , PUNCT cana-3280 229 14 and and CCONJ cana-3280 229 15 p. p. PROPN cana-3280 229 16 karthikeyan karthikeyan PROPN cana-3280 229 17 , , PUNCT cana-3280 229 18 β€œ " PUNCT cana-3280 229 19 investigation investigation NOUN cana-3280 229 20 on on ADP cana-3280 229 21 human human ADJ cana-3280 229 22 activity activity NOUN cana-3280 229 23 recognition recognition NOUN cana-3280 229 24 using use VERB cana-3280 229 25 deep deep ADJ cana-3280 229 26 learning learning NOUN cana-3280 229 27 , , PUNCT cana-3280 229 28 ” " PUNCT cana-3280 229 29 procedia procedia NOUN cana-3280 229 30 comput comput NOUN cana-3280 229 31 . . PUNCT cana-3280 230 1 sci sci PROPN cana-3280 230 2 . . PROPN cana-3280 230 3 , , PUNCT cana-3280 230 4 vol vol NOUN cana-3280 230 5 . . PROPN cana-3280 231 1 204 204 NUM cana-3280 231 2 , , PUNCT cana-3280 231 3 pp pp ADJ cana-3280 231 4 . . PUNCT cana-3280 232 1 73–80 73–80 NUM cana-3280 232 2 , , PUNCT cana-3280 232 3 2022 2022 NUM cana-3280 232 4 , , PUNCT cana-3280 232 5 doi doi NOUN cana-3280 232 6 : : PUNCT cana-3280 232 7 10.1016 10.1016 NUM cana-3280 232 8 / / SYM cana-3280 232 9 j.procs.2022.08.009 j.procs.2022.08.009 PROPN cana-3280 232 10 . . PUNCT cana-3280 233 1 [ [ X cana-3280 233 2 21 21 NUM cana-3280 233 3 ] ] PUNCT cana-3280 233 4 p. p. PROPN cana-3280 233 5 kumar kumar PROPN cana-3280 233 6 and and CCONJ cana-3280 233 7 s. s. PROPN cana-3280 233 8 suresh suresh PROPN cana-3280 233 9 , , PUNCT cana-3280 233 10 β€œ " PUNCT cana-3280 233 11 flaap flaap ADV cana-3280 233 12 : : PUNCT cana-3280 233 13 an an DET cana-3280 233 14 open open ADJ cana-3280 233 15 human human ADJ cana-3280 233 16 activity activity NOUN cana-3280 233 17 recognition recognition NOUN cana-3280 233 18 ( ( PUNCT cana-3280 233 19 har har NOUN cana-3280 233 20 ) ) PUNCT cana-3280 233 21 dataset dataset NOUN cana-3280 233 22 for for ADP cana-3280 233 23 learning learn VERB cana-3280 233 24 and and CCONJ cana-3280 233 25 finding find VERB cana-3280 233 26 the the DET cana-3280 233 27 associated associated ADJ cana-3280 233 28 activity activity NOUN cana-3280 233 29 patterns pattern NOUN cana-3280 233 30 , , PUNCT cana-3280 233 31 ” " PUNCT cana-3280 233 32 procedia procedia NOUN cana-3280 233 33 comput comput NOUN cana-3280 233 34 . . PUNCT cana-3280 234 1 sci sci PROPN cana-3280 234 2 . . PROPN cana-3280 234 3 , , PUNCT cana-3280 234 4 vol vol NOUN cana-3280 234 5 . . PROPN cana-3280 234 6 212 212 NUM cana-3280 234 7 , , PUNCT cana-3280 234 8 no no INTJ cana-3280 234 9 . . PUNCT cana-3280 235 1 c c X cana-3280 235 2 , , PUNCT cana-3280 235 3 pp pp ADJ cana-3280 235 4 . . PUNCT cana-3280 236 1 64–73 64–73 NUM cana-3280 236 2 , , PUNCT cana-3280 236 3 2022 2022 NUM cana-3280 236 4 , , PUNCT cana-3280 236 5 doi doi NOUN cana-3280 236 6 : : PUNCT cana-3280 236 7 10.1016 10.1016 NUM cana-3280 236 8 / / SYM cana-3280 236 9 j.procs.2022.10.208 j.procs.2022.10.208 PROPN cana-3280 236 10 . . PUNCT cana-3280 237 1 [ [ X cana-3280 237 2 22 22 NUM cana-3280 237 3 ] ] PUNCT cana-3280 237 4 β€œ " PUNCT cana-3280 237 5 in in ADP cana-3280 237 6 machine machine NOUN cana-3280 237 7 learning learning NOUN cana-3280 237 8 , , PUNCT cana-3280 237 9 when when SCONJ cana-3280 237 10 is be AUX cana-3280 237 11 one one NUM cana-3280 237 12 hot hot ADJ cana-3280 237 13 encoding encoding NOUN cana-3280 237 14 better well ADV cana-3280 237 15 than than ADP cana-3280 237 16 target target NOUN cana-3280 237 17 ( ( PUNCT cana-3280 237 18 mean mean NOUN cana-3280 237 19 ) ) PUNCT cana-3280 237 20 encoding encoding NOUN cana-3280 237 21 ? ? PUNCT cana-3280 238 1 why why SCONJ cana-3280 238 2 would would AUX cana-3280 238 3 you you PRON cana-3280 238 4 ever ever ADV cana-3280 238 5 use use VERB cana-3280 238 6 ohe ohe NOUN cana-3280 238 7 over over ADP cana-3280 238 8 target target NOUN cana-3280 238 9 encoding encoding NOUN cana-3280 238 10 ? ? PUNCT cana-3280 238 11 quora quora NOUN cana-3280 238 12 . . PUNCT cana-3280 238 13 ” " PUNCT cana-3280 239 1 https://www.quora.com/in-machine-learning-when-is-one-hot-encoding-better-thantarget-mean-encoding-why-would-you-ever-use-ohe-over-target-encoding https://www.quora.com/in-machine-learning-when-is-one-hot-encoding-better-thantarget-mean-encoding-why-would-you-ever-use-ohe-over-target-encode VERB cana-3280 239 2 ( ( PUNCT cana-3280 239 3 accessed access VERB cana-3280 239 4 apr apr NOUN cana-3280 239 5 . . PROPN cana-3280 239 6 25 25 NUM cana-3280 239 7 , , PUNCT cana-3280 239 8 2023 2023 NUM cana-3280 239 9 ) ) PUNCT cana-3280 239 10 . . PUNCT cana-3280 240 1 [ [ X cana-3280 240 2 23 23 NUM cana-3280 240 3 ] ] PUNCT cana-3280 240 4 β€œ " PUNCT cana-3280 240 5 sklearn.preprocessing.onehotencoder sklearn.preprocessing.onehotencoder NOUN cana-3280 240 6 β€” β€” PUNCT cana-3280 240 7 scikit scikit NOUN cana-3280 240 8 - - PUNCT cana-3280 240 9 learn learn VERB cana-3280 240 10 1.2.2 1.2.2 NUM cana-3280 240 11 documentation documentation NOUN cana-3280 240 12 . . PUNCT cana-3280 240 13 ” " PUNCT cana-3280 241 1 https://scikitlearn.org/stable/modules/generated/sklearn.preprocessing.onehotencoder.html https://scikitlearn.org/stable/modules/generated/sklearn.preprocessing.onehotencoder.html NOUN cana-3280 241 2 ( ( PUNCT cana-3280 241 3 accessed access VERB cana-3280 241 4 apr apr NOUN cana-3280 241 5 . . PROPN cana-3280 241 6 25 25 NUM cana-3280 241 7 , , PUNCT cana-3280 241 8 2023 2023 NUM cana-3280 241 9 ) ) PUNCT cana-3280 241 10 . . PUNCT cana-3280 242 1 [ [ X cana-3280 242 2 24 24 NUM cana-3280 242 3 ] ] PUNCT cana-3280 242 4 β€œ " PUNCT cana-3280 242 5 crcv crcv NOUN cana-3280 242 6 | | ADV cana-3280 242 7 center center NOUN cana-3280 242 8 for for ADP cana-3280 242 9 research research NOUN cana-3280 242 10 in in ADP cana-3280 242 11 computer computer NOUN cana-3280 242 12 vision vision NOUN cana-3280 242 13 at at ADP cana-3280 242 14 the the DET cana-3280 242 15 university university PROPN cana-3280 242 16 of of ADP cana-3280 242 17 central central PROPN cana-3280 242 18 florida florida PROPN cana-3280 242 19 . . PUNCT cana-3280 242 20 ” " PUNCT cana-3280 243 1 https://www.crcv.ucf.edu/data/ucf50.php https://www.crcv.ucf.edu/data/ucf50.php INTJ cana-3280 243 2 ( ( PUNCT cana-3280 243 3 accessed access VERB cana-3280 243 4 apr apr NOUN cana-3280 243 5 . . PROPN cana-3280 243 6 25 25 NUM cana-3280 243 7 , , PUNCT cana-3280 243 8 2023 2023 NUM cana-3280 243 9 ) ) PUNCT cana-3280 243 10 . . PUNCT cana-3280 244 1 [ [ X cana-3280 244 2 25 25 NUM cana-3280 244 3 ] ] PUNCT cana-3280 244 4 β€œ " PUNCT cana-3280 244 5 5 5 X cana-3280 244 6 . . X cana-3280 244 7 cnn cnn PROPN cana-3280 244 8 - - PUNCT cana-3280 244 9 lstm lstm PROPN cana-3280 244 10 β€” β€” PUNCT cana-3280 244 11 pseudolab pseudolab NOUN cana-3280 244 12 tutorial tutorial NOUN cana-3280 244 13 book book NOUN cana-3280 244 14 . . PUNCT cana-3280 244 15 ” " PUNCT cana-3280 245 1 https://pseudo-lab.github.io/tutorial-book-en/chapters/en/timeseries/ch5-cnn-lstm.html https://pseudo-lab.github.io/tutorial-book-en/chapters/en/timeseries/ch5-cnn-lstm.html PROPN cana-3280 245 2 ( ( PUNCT cana-3280 245 3 accessed access VERB cana-3280 245 4 apr apr NOUN cana-3280 245 5 . . PROPN cana-3280 245 6 25 25 NUM cana-3280 245 7 , , PUNCT cana-3280 245 8 2023 2023 NUM cana-3280 245 9 ) ) PUNCT cana-3280 245 10 . . PUNCT cana-3280 246 1 [ [ X cana-3280 246 2 26 26 NUM cana-3280 246 3 ] ] X cana-3280 246 4 s. s. PROPN cana-3280 246 5 anoopa anoopa PROPN cana-3280 246 6 , , PUNCT cana-3280 246 7 a. a. NOUN cana-3280 246 8 salim salim PROPN cana-3280 246 9 , , PUNCT cana-3280 246 10 and and CCONJ cana-3280 246 11 s. s. PROPN cana-3280 246 12 nadeera nadeera PROPN cana-3280 246 13 beevi beevi PROPN cana-3280 246 14 , , PUNCT cana-3280 246 15 β€œ " PUNCT cana-3280 246 16 advanced advanced ADJ cana-3280 246 17 video video NOUN cana-3280 246 18 anomaly anomaly NOUN cana-3280 246 19 detection detection NOUN cana-3280 246 20 using use VERB cana-3280 246 21 2d 2d NUM cana-3280 246 22 cnn cnn PROPN cana-3280 246 23 and and CCONJ cana-3280 246 24 stacked stack VERB cana-3280 246 25 lstm lstm NOUN cana-3280 246 26 with with ADP cana-3280 246 27 deep deep ADJ cana-3280 246 28 active active ADJ cana-3280 246 29 learning learning NOUN cana-3280 246 30 - - PUNCT cana-3280 246 31 based base VERB cana-3280 246 32 model model NOUN cana-3280 246 33 , , PUNCT cana-3280 246 34 ” " PUNCT cana-3280 246 35 kuwait kuwait PROPN cana-3280 246 36 j. j. PROPN cana-3280 246 37 sci sci PROPN cana-3280 246 38 . . PROPN cana-3280 246 39 , , PUNCT cana-3280 246 40 vol vol NOUN cana-3280 246 41 . . PROPN cana-3280 246 42 49 49 NUM cana-3280 246 43 , , PUNCT cana-3280 246 44 jun jun PROPN cana-3280 246 45 . . PROPN cana-3280 246 46 2022 2022 NUM cana-3280 246 47 , , PUNCT cana-3280 246 48 doi doi NOUN cana-3280 246 49 : : PUNCT cana-3280 246 50 10.48129 10.48129 NUM cana-3280 246 51 / / SYM cana-3280 246 52 kjs.splml.19159 kjs.splml.19159 PROPN cana-3280 246 53 . . PUNCT cana-3280 247 1 [ [ X cana-3280 247 2 27 27 NUM cana-3280 247 3 ] ] PUNCT cana-3280 247 4 β€œ " PUNCT cana-3280 247 5 long long ADJ cana-3280 247 6 - - PUNCT cana-3280 247 7 term term NOUN cana-3280 247 8 recurrent recurrent ADJ cana-3280 247 9 convolutional convolutional ADJ cana-3280 247 10 network network NOUN cana-3280 247 11 for for ADP cana-3280 247 12 video video NOUN cana-3280 247 13 regression regression NOUN cana-3280 247 14 | | ADV cana-3280 247 15 by by ADP cana-3280 247 16 alexander alexander PROPN cana-3280 247 17 golubev golubev PROPN cana-3280 248 1 | | ADV cana-3280 248 2 towards towards ADP cana-3280 248 3 data datum NOUN cana-3280 248 4 science science NOUN cana-3280 248 5 . . PUNCT cana-3280 248 6 ” " PUNCT cana-3280 249 1 https://towardsdatascience.com/long-term-recurrent-convolutional-network-for-video-regression12138f8b4713 https://towardsdatascience.com/long-term-recurrent-convolutional-network-for-video-regression12138f8b4713 NOUN cana-3280 249 2 ( ( PUNCT cana-3280 249 3 accessed access VERB cana-3280 249 4 apr apr NOUN cana-3280 249 5 . . PROPN cana-3280 249 6 25 25 NUM cana-3280 249 7 , , PUNCT cana-3280 249 8 2023 2023 NUM cana-3280 249 9 ) ) PUNCT cana-3280 249 10 . . PUNCT