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The experimental results indicate the potential and strengths of the proposed 1D-HARCapsNet that achieved enhanced performance with an accuracy of 98.67%, precision of 98.66%, recall of 98.67%, and F1-measure of 0.987 which shows major performance enhancement compared to the Conventional CapsNet (accuracy 90.11%, precision 91.88%, recall 89.94%, and F1-measure 0.93).<\/jats:p>","DOI":"10.1007\/s40747-021-00508-5","type":"journal-article","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T09:02:52Z","timestamp":1631005372000},"page":"3535-3546","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Intelligent system for human activity recognition in IoT environment"],"prefix":"10.1007","volume":"9","author":[{"given":"Hassan","family":"Khaled","sequence":"first","affiliation":[]},{"given":"Osama","family":"Abu-Elnasr","sequence":"additional","affiliation":[]},{"given":"Samir","family":"Elmougy","sequence":"additional","affiliation":[]},{"given":"A. 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