{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:54:41Z","timestamp":1742997281572,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031708183"},{"type":"electronic","value":"9783031708190"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-70819-0_28","type":"book-chapter","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T16:02:28Z","timestamp":1725552148000},"page":"360-377","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Activity Recognition Through Joint Utilization of Decimal Descriptors and Temporal Binary Motions"],"prefix":"10.1007","author":[{"given":"Mariem","family":"Gnouma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samah","family":"Yahia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ridha","family":"Ejbali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mourad","family":"Zaied","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,31]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","first-page":"3090343","DOI":"10.1155\/2017\/3090343","volume":"2017","author":"S Zhang","year":"2017","unstructured":"Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., Li, Z.: A review on human activity recognition using vision-based method. J. Healthc. Eng. 2017, 3090343 (2017)","journal-title":"J. Healthc. Eng."},{"issue":"1","key":"28_CR2","doi-asserted-by":"publisher","first-page":"1965","DOI":"10.1007\/s11042-023-15443-5","volume":"83","author":"G Bhola","year":"2024","unstructured":"Bhola, G., Vishwakarma, D.K.: A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects. Multimedia Tools and Applications 83(1), 1965\u20132005 (2024)","journal-title":"Multimedia Tools and Applications"},{"issue":"2","key":"28_CR3","doi-asserted-by":"publisher","first-page":"56","DOI":"10.38094\/jastt1224","volume":"1","author":"R Zebari","year":"2020","unstructured":"Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., Saeed, J.: A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. J. Appli. Sci. Technolo. Trends 1(2), 56\u201370 (2020)","journal-title":"J. Appli. Sci. Technolo. Trends"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Wu, D.; Sharma, N.; Blumenstein, M.:\u00a0 Recent advances in video-based human action recognition using deep learning: A review. In: Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 14\u201319 May 2017, pp. 2865\u20132872\u00a0(2017)","DOI":"10.1109\/IJCNN.2017.7966210"},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"Sargano,\u00a0A.B., et al.: Human action recognition using transfer learning with deep representations. In:\u00a0 International Joint Conference on Neural Network (IJCNN), pp. 463\u2013469\u00a0(2017)","DOI":"10.1109\/IJCNN.2017.7965890"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Mathe,E., et al.:\u00a0 A deep learning approach for human action recognition using skeletal information. In: GeNeDis, P. V. (ed.),\u00a0 pp. 105\u2013114\u00a0(2018)","DOI":"10.1007\/978-3-030-32622-7_9"},{"key":"28_CR7","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.neucom.2020.06.032","volume":"410","author":"Z Zhang","year":"2020","unstructured":"Zhang, Z., Lv, Z., Gan, C., Zhu, Q.: Human action recognition using convolutional LSTM and fully-connected LSTM with different attentions. Neurocomputing 410, 304\u2013316 (2020)","journal-title":"Neurocomputing"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Mukherjee, D., et al.: EnsemConvNet: a deep learning approach for human activity recognition using smartphone sensors for healthcare applications. Multimedia Tools Appl. 79\u00a0(2020)","DOI":"10.1007\/s11042-020-09537-7"},{"key":"28_CR9","doi-asserted-by":"publisher","first-page":"105820","DOI":"10.1016\/j.asoc.2019.105820","volume":"86","author":"C Dai","year":"2020","unstructured":"Dai, C., Liu, X., Lai, J.: Human action recognition using two-stream attention based LSTM networks. Appl. Soft Comput. 86, 105820 (2020)","journal-title":"Appl. Soft Comput."},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Khan,\u00a0M.A., et al.: Emergence of a novel coronavirus, severe acute respiratory syndrome coronavirus 2: biology and therapeutic options. J. Clin. Microbiol. 58(5)\u00a0 (2020)","DOI":"10.1128\/JCM.00187-20"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Nabati, M., Navidan, H., Shahbazian, R., Ghorashi, S.A., Windridge, D.: Using synthetic data to enhance the accuracy of fingerprint-based localization: A Deep Learning Approach. IEEE Sensors Lett. 2020(4), 6000204 (2020)","DOI":"10.1109\/LSENS.2020.2971555"},{"issue":"7","key":"28_CR12","doi-asserted-by":"publisher","first-page":"3591","DOI":"10.3390\/s23073591","volume":"23","author":"M Chahoushi","year":"2023","unstructured":"Chahoushi, M., Nabati, M., Asvadi, R., Ghorashi, S.A.: CSI-Based human activity recognition using multi-input multi-output autoencoder and fine-tuning. Sensors 23(7), 3591 (2023)","journal-title":"Sensors"},{"key":"28_CR13","doi-asserted-by":"publisher","first-page":"76592","DOI":"10.1109\/ACCESS.2021.3082627","volume":"9","author":"X Cheng","year":"2021","unstructured":"Cheng, X., Huang, B., Zong, J.: Device-free human activity recognition based on GMM-HMM using channel state information. IEEE Access 9, 76592\u201376601 (2021)","journal-title":"IEEE Access"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Gnouma, M., Ejbali, R.,\u00a0 Zaied, M.:\u00a0 A temporal human activity recognition based on stacked auto encoder and extreme learning machine. In: 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 1571\u20131576. IEEE\u00a0(2023)","DOI":"10.1109\/CoDIT58514.2023.10284218"},{"issue":"23","key":"28_CR15","doi-asserted-by":"publisher","first-page":"30769","DOI":"10.1007\/s11042-018-6160-9","volume":"77","author":"S Yahia","year":"2018","unstructured":"Yahia, S., Salem, Y.B., Abdelkrim, M.N.: Texture analysis of magnetic resonance brain images to assess multiple sclerosis lesions. Multimed. Tools Appl. 77(23), 30769\u201330789 (2018)","journal-title":"Multimed. Tools Appl."},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Yahia, S, Yassine, B.S., Abdelktim Naceur, A.M.:\u00a0 Multiple sclerosis lesions detection from noisy magnetic resonance brain images tissue. In: International Multi-Conference on Systems, Signals & Devices (SSD), pp. 240\u2013245. IEEE\u00a0(2018)","DOI":"10.1109\/SSD.2018.8570679"},{"key":"28_CR17","doi-asserted-by":"publisher","first-page":"14425","DOI":"10.1007\/s11042-018-6768-9","volume":"78","author":"Z Youbi","year":"2019","unstructured":"Youbi, Z., Boubchir, L., Boukrouche, A.: Human ear recognition based on local multi-scale LBP features with city-block distance. Multimedia Tools Appli. 78, 14425\u201314441 (2019)","journal-title":"Multimedia Tools Appli."},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Gnouma, M., Ladjailia, A., Ejbali, R., Zaied, M.:\u00a0 Stacked sparse autoencoder and history of binary motion image for human activity recognition. Multimedia Tools Appli. 78(2)\u00a0(2019)","DOI":"10.1007\/s11042-018-6273-1"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Shaikh, I.A.K., Krishna, P.V., Biswal, S.G., Kumar, A.S., Baranidharan, S.,\u00a0 Singh, K.: Bayesian optimization with stacked sparse autoencoder based cryptocurrency price prediction model. In: 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 653\u2013658. IEEE\u00a0 (January 2023)","DOI":"10.1109\/ICSSIT55814.2023.10061153"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local SVM approach. In: Proc. 17th International Conference on Pattern Recognition ICPR, vol. 3, pp. 32\u201336.\u00a0IEEE (2004)","DOI":"10.1109\/ICPR.2004.1334462"},{"issue":"2\u20133","key":"28_CR21","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.cviu.2006.07.013","volume":"104","author":"D Weinland","year":"2006","unstructured":"Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Comput. Vision Image Underst. 104(2\u20133), 249\u2013257 (2006)","journal-title":"Comput. Vision Image Underst."},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.:\u00a0 Study of human action recognition based on improved spatio-temporal features. Human Motion Sensing Recogn. Fuzzy Qualit. Approach, 233\u2013250\u00a0 (2017)","DOI":"10.1007\/978-3-662-53692-6_11"},{"issue":"4","key":"28_CR23","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1049\/iet-cvi.2015.0233","volume":"10","author":"S Chun","year":"2016","unstructured":"Chun, S., Lee, C.S.: Human action recognition using histogram of motion intensity and direction from multiple views. IET Comput. Vision 10(4), 250\u2013257 (2016)","journal-title":"IET Comput. Vision"},{"issue":"2","key":"28_CR24","doi-asserted-by":"publisher","first-page":"327","DOI":"10.4218\/etrij.2019-0510","volume":"44","author":"N Nida","year":"2022","unstructured":"Nida, N., Yousaf, M.H., Irtaza, A., Velastin, S.A.: Video augmentation technique for human action recognition using genetic algorithm. ETRI J. 44(2), 327\u2013338 (2022)","journal-title":"ETRI J."},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Kiran, S., et al.:\u00a0 Multi-layered deep learning features fusion for human action recognition.\u00a0Comput. Mater. Continua 69(3)\u00a0 (2021)","DOI":"10.32604\/cmc.2021.017800"},{"issue":"5","key":"28_CR26","doi-asserted-by":"publisher","first-page":"2745","DOI":"10.3390\/s23052745","volume":"23","author":"NUR Malik","year":"2023","unstructured":"Malik, N.U.R., Sheikh, U.U., Abu-Bakar, S.A.R., Channa, A.: Multi-view human action recognition using skeleton based-fineknn with extraneous frame scrapping technique. Sensors 23(5), 2745 (2023)","journal-title":"Sensors"}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70819-0_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T20:33:00Z","timestamp":1732739580000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70819-0_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031708183","9783031708190"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70819-0_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"31 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leipzig","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}