{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T07:52:54Z","timestamp":1770277974174,"version":"3.49.0"},"reference-count":13,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2022,8,23]]},"abstract":"<jats:p>Deep learning techniques have recently demonstrated their ability to be applied in any field, including image processing, natural language processing, speech recognition, and many other real-world problem-solving applications. Human activity recognition (HAR), on the other hand, has become a popular research topic due to its wide range of applications. The researchers began working on the new ideas by combining the two emerging areas to solve HAR problems using deep learning. Recurrent neural networks (RNNs) in deep learning (DL) provide higher opportunity in recognizing the abnormal behavior of humans to avoid any kind of security issues. The present study proposed a deep network architecture based on one of the techniques of deep learning named as residual bidirectional long-term memory (LSTM). The new network is capable of avoiding gradient vanishing in both temporal and spatial dimensions with a view to increase the rate of recognition. To understand the complexity of activities recognition and classification, two LSTM models, basic model and the proposed model, were used. Later, a comparative analysis is performed to understand the efficiencies of the models during the classification of five human activities like abuse, arrest, arson, assault, and fighting images classification. The basic LSTM model has achieved a training accuracy of just 18% and testing accuracy of 21% with higher training and classification loss values. But the proposed LSTM model has outperformed the basic model while achieving 100% classification accuracy. Finally, the observations have proved that the proposed LSTM model is best suitable in recognizing and classifying the human activities well even for real-time videos.<\/jats:p>","DOI":"10.1155\/2022\/1681096","type":"journal-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T23:50:57Z","timestamp":1661298657000},"page":"1-8","source":"Crossref","is-referenced-by-count":4,"title":["LSTM-Based Neural Network to Recognize Human Activities Using Deep Learning Techniques"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4027-5212","authenticated-orcid":true,"given":"Sunitha","family":"Sabbu","sequence":"first","affiliation":[{"name":"Koneru Lakshmaiah Education Institute, Vaddeswaram, Andhra Pradesh, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5896-4094","authenticated-orcid":true,"given":"Vithya","family":"Ganesan","sequence":"additional","affiliation":[{"name":"Koneru Lakshmaiah Education Institute, Vaddeswaram, Andhra Pradesh, India"}]}],"member":"311","reference":[{"key":"1","first-page":"1729","article-title":"Feature learning for activity recognition inUbiquitous computing","author":"T. Pl\u00f6tz"},{"key":"2","article-title":"Deep ActivityRecognition models with triaxial accelerometers","author":"M. A. Alsheikh"},{"key":"3","first-page":"197","article-title":"ConvolutionalNeural networks for human activity recognition using mobile sensors","author":"M. Zeng"},{"key":"4","first-page":"1488","article-title":"A deep learning approach to human activity recognition based onSingle accelerometer","author":"Y. Chen"},{"key":"5","volume-title":"Human Activity Recognition with Two Body-WornAccelerometer Sensors","author":"H.-O. Hessen","year":"2015"},{"key":"6","article-title":"Deep convolutional neuralnetworks on multichannel time series for human activity recognition","author":"J. B. Yang"},{"key":"7","first-page":"71","article-title":"Deep learning for human activity recognition: aresource efficient implementation on low-power devices","author":"D. Ravi"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1109\/29.21701"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2015.06.009"},{"key":"10","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/ACCESS.2017.2778011","article-title":"Action recognition in video sequences using deep Bi-directional LSTM with CNN features, special section on visual surveillance and biometrics: practices, challenges, and possibilities","volume":"6","author":"U. Amin","year":"2018","journal-title":"IEEE Access"},{"key":"11","article-title":"A critical review of recurrent neural networks for sequence learning","author":"Z. C. Lipton","year":"2015"},{"key":"12","first-page":"753","article-title":"Bidirectional lstm networks for improved phoneme classification and recognition","author":"A. Graves"},{"key":"13","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"A. Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"}],"container-title":["Applied Computational Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2022\/1681096.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2022\/1681096.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/acisc\/2022\/1681096.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T23:51:04Z","timestamp":1661298664000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/acisc\/2022\/1681096\/"}},"subtitle":[],"editor":[{"given":"Ridha","family":"Ejbali","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,8,23]]},"references-count":13,"alternative-id":["1681096","1681096"],"URL":"https:\/\/doi.org\/10.1155\/2022\/1681096","relation":{},"ISSN":["1687-9732","1687-9724"],"issn-type":[{"value":"1687-9732","type":"electronic"},{"value":"1687-9724","type":"print"}],"subject":[],"published":{"date-parts":[[2022,8,23]]}}}