Sensor-based human motion detection requires the subtle amount of knowledge about various human activities from fitted sensor observations and readings. The prevalent pattern recognition methodologies have made immense progress over recent years. Nonetheless, these kind of methods usually rely on the particular heuristic variable extraction, which could inhibit generalization realization. This paper presents a distributed and parallel decision forest approach for modeling the Human Activity Recognition Using Smartphones Data. We made an attempt to achieve an optimal generalization performance with possible reduction in overfitting. Later, we compared the performance of proposed procedure with some existing approaches. It is observed that our adopted procedure outperforms with comparatively better statistical performance measures. It also gained 4.7x speed up in computation.
In this paper we propose the two stage opportunistic sampling technique for the detection and classification of network anomalies. Literature review indicates the application of one stage sampling for network anomaly detection. It is observed that for specific-purpose applications such as anomaly detection, a large fraction of information is contained in a small fraction of flows. We demonstrate that by using opportunistic and preferential sampling, the appearance and detection of anomalies within the sampled data set can be improved. We implement the two stage sampling and show that the results obtained are more effective. The evaluation of intelligent sampling techniques for improved anomaly detection is based on the application of an entropy-based technique on a packet trace. The proposed two-stage sampling reduces the time taken for the process when compared to the one stage sampling. We have also evaluated the results with different entropy values and observed the variation in flow distribution characteristics.
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