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dc.contributor.author Shiranthika, C
dc.contributor.author Premakumara, N
dc.contributor.author Chiu, HL
dc.contributor.author Samani, H
dc.contributor.author Shyalika, C
dc.contributor.author Yang, CY
dc.contributor.editor Karunananda, AS
dc.contributor.editor Karunananda, AS
dc.contributor.editor Talagala, PD
dc.date.accessioned 2022-11-16T04:04:22Z
dc.date.available 2022-11-16T04:04:22Z
dc.date.issued 2020-12
dc.identifier.citation C. Shiranthika, N. Premakumara, H. -L. Chiu, H. Samani, C. Shyalika and C. -Y. Yang, "Human Activity Recognition Using CNN & LSTM," 2020 5th International Conference on Information Technology Research (ICITR), 2020, pp. 1-6, doi: 10.1109/ICITR51448.2020.9310792. en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/19515
dc.description.abstract In identifying objects, understanding the world, analyzing time series and predicting future sequences, the recent developments in Artificial Intelligence (AI) have made human beings more inclined towards novel research goals. There is a growing interest in Recurrent Neural Networks (RNN) by AI researchers today, which includes major applications in the fields of speech recognition, language modeling, video processing and time series analysis. Recognition of Human Behavior or the Human Activity Recognition (HAR) is one of the difficult issues in this wonderful AI field that seeks answers. As an assistive technology combined with innovations such as the Internet of Things (IoT), it can be primarily used for eldercare and childcare. HAR also covers a broad variety of real-life applications, ranging from healthcare to personal fitness, gaming, military applications, security fields, etc. HAR can be achieved with sensors, images, smartphones or videos where the advancement of Human Computer Interaction (HCI) technology has become more popular for capturing behaviors using sensors such as accelerometers and gyroscopes. This paper introduces an approach that uses CNN and Long Short-Term Memory (LSTM) to predict human behaviors on the basis of the WISDM dataset. en_US
dc.language.iso en en_US
dc.publisher Faculty of Information Technology, University of Moratuwa. en_US
dc.relation.uri https://ieeexplore.ieee.org/document/9310792 en_US
dc.subject Human activity recognition en_US
dc.subject Convolutional neural networks (CNN) en_US
dc.subject Long short-term memory (LSTM) en_US
dc.title Human activity recognition using cnn & lstm en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty IT en_US
dc.identifier.department Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.identifier.year 2020 en_US
dc.identifier.conference 5th International Conference in Information Technology Research 2020 en_US
dc.identifier.place Moratuwa, Sri Lanka en_US
dc.identifier.proceeding Proceedings of the 5th International Conference in Information Technology Research 2020 en_US
dc.identifier.doi doi: 10.1109/ICITR51448.2020.9310792 en_US


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  • ICITR - 2020 [27]
    International Conference on Information Technology Research (ICITR)

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