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Bakariya, Brijesh
- Measurement of Distance from Page Sequences Using Dynamic Programming
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Authors
Affiliations
1 Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, IN
2 Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, M.P., IN
1 Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, IN
2 Department of Computer Applications, Maulana Azad National Institute of Technology, Bhopal, M.P., IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 8 (2013), Pagination: 306-311Abstract
Internet is playing a vital role for accessing information, because lots of information is available on internet. Lots of data are rapidly growing, but the data which is resided on the web include irrelevant information, it contains different types of data format. Due to heterogeneity of data it is very challenging task to retrieve relevant information from web data. Using web usage mining technique, mine the relevant information from large amount of data available in the web logs format that enclose intrinsic information regarding web pages accessed. Because of this large amount of web log data, it is better to deal with small set of data at a time, instead of handling with whole data jointly. Now we need to find the distance between two user sessions, using some distance similarity function can be accomplish this kind of tasks. Clustering of users tends to establish groups of users exhibiting similar browsing patterns. In this paper we propose novel algorithm, for measuring the similarity between two user sessions based on sequence alignment that uses the Longest Common Subsequence method.Keywords
Clustering, Longest Common Subsequence, Web Logs, Web Usage Mining.- Comparative Analysis of Recurrent Neural Network Architectures and Hyperparameters for Human Activity Recognition Using Wearable Sensors"
Abstract Views :122 |
PDF Views:0
Authors
Affiliations
1 Research Scholar, Programmer, I. K. Gujral Punjab Technical University, Kapurthala, IN
2 Assistant Professor, I. K. Gujral Punjab Technical University, Kapurthala, IN
1 Research Scholar, Programmer, I. K. Gujral Punjab Technical University, Kapurthala, IN
2 Assistant Professor, I. K. Gujral Punjab Technical University, Kapurthala, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 276-288Abstract
Human activity recognition (HAR) is a significant area of research with numerous applications in healthcare, athletics performance monitoring, and elderly care. The potential for HAR with ubiquitous sensors to enhance human performance and quality of life has attracted significant research interest. Recurrent Neural Networks (RNNs) have emerged as a powerful tool for HAR, as they can model sequential data and capture temporal dependencies in time-series data. Using accelerometer data from wearable sensors, this study investigates the efficacy of various recurrent neural network (RNN) architectures and hyperparameters for HAR. Specifically, we compare the performance of three RNN architectures (Simple RNN, LSTM, and GRU) and investigate the impact of hidden units and sequence length on the accuracy of the models. We use the publicly available HARUS dataset, which consists of accelerometer data collected from 30 subjects performing six different activities. Our results show that the LSTM architecture outperforms the other two architectures, achieving an accuracy of 95.0% on the HARUS dataset. We also discover that increasing the number of hidden units generally improves accuracy, with 128 hidden units producing the greatest results. Increasing the sequence length also leads to higher accuracy, but increasing it beyond a certain point can lead to overfitting. In addition, a separate study found that their RNN model obtained an overall accuracy of 99.54 percent on the test set for recognizing various activities using accelerometer data from a wearable sensor. The model performed particularly well for walking and jogging activities, as well as standing and sitting activities, and performed reasonably well for more complex activities such as walking upstairs and downstairs. Our findings indicate that LSTM is a suitable architecture for HAR tasks and that the number of concealed units and sequence length are crucial hyperparameters to consider. Our findings contribute to the existing literature on HAR by revealing the optimal architecture and hyperparameters for the accurate recognition of human activities from accelerometer data collected by wearable sensors.Keywords
Human Activity Recognition, HARUS Dataset, Wearable Sensors, Accelerometer Data, Recurrent Neural Networks, RNN Architectures, Simple RNN, LSTM, GRU, Hyperparameters, Hidden Units, Sequence Length, Overfitting, Temporal Dependencies, Time-Series Data, Accuracy.References
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- Gao, L., Li, P., Li, Y., Wu, S., & Chen, L. (2021). A novel hybrid model based on LSTM and attention mechanism for human activity recognition. Information Sciences, 564, 60-72.
- Xia, Z., Ni, X., Liu, C., & Sun, J. (2021). Deep neural networks for human activity recognition based on smartwatch sensor data. Sensors, 21(1), 234.
- Zhu, W., Qiu, J., Zhou, F., & Wan, Y. (2020). Human activity recognition based on wearable sensors using a hybrid CNN-LSTM model. IEEE Access, 8, 157801-157810.
- Zhu, Y., Wu, X., & Liu, X. (2020). A hybrid CNN-LSTM model for human activity recognition. IEEE Access, 8, 55817-55827.
- Zhang, J., Yin, J., & Chen, K. (2019). An improved CNN for human activity recognition using mobile phone sensor data. Sensors, 19(7), 1508.
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