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In order to effectively analyze tens of thousands of new, potentially malicious PDF files on a daily basis, anti-virus vendors have integrated a component of a detection model based on machine learning (ML) and rule-based algorithms [3] into the core of their signature repository update activities.
Thus, this study presents a novel fault detection model based on kernel independent component analysis and principal component analysis (KICA PCA) monitoring model for condition process of HGU.
Wang et al. [5] developed a conflict-point detection model based on microsimulation of heterogeneous traffic (i.e., motorized and non-motorized vehicles) in China.
In this paper, we propose a new computational saliency detection model based on the deep features of RGB images and depth images within a Bayesian framework.
In the paper "Network anomaly detection based on wavelet analysis," coauthored by Wei Lu and Ali Ghorbani, the authors propose a new network anomaly detection model based on wavelet approximation and system identification theory.
Kundel, Nodine, and Carmody (1978) developed a nodule detection model based on the assumption that prolonged dwell times indicate intensive processing of visual data to enable classification of false-negative responses to pulmonary nodules into different types of error.
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While existing studies have explored this issue, they have, in most cases, either focused on empirical analysis of such cases or proposed detection models based on certain assumptions of the market.
In fact, this causes existing community detection models based on both cascades and user connections, such as [2], not applicable.
Recently, Chen and Huang proposed two crowd behaviour detection models based on motion [111] and visual with graph and matching [112].
As stated in Section 3, our proposed EIF approaches can be applied to any QTL detection models based on LOD score.
The normalized values were used for calculation of log2 ratio values and used for CNV detection using a segmentation model based on a Gaussian framework [ 47].
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