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An intrusion detection system (IDS) monitors the network traffic looking for suspicious or malicious activities or policy violations, which could represent an attack or unauthorized access. Traditional systems were designed to detect known attacks but cannot identify unknown threats. They most commonly detect known threats based on predefined rules or behavioral analysis through baselining the network...
The use of depth sensors in activity recognition is a technology that emerges in human computer interaction and motion recognition. In this study, an approach to identify single-person activities using deep learning on depth image sequences is presented. First, a 3D volumetric template is generated using skeletal information obtained from a depth video. The generated 3D volume is used for extracting...
In this work, we develop a video surveillance system to detect the disappearance of the objects selected by an operator. Proposed method is optimized to minimize the effects of shadows, partial occlusions and changes in illumination to minimize false alarms. The system is trained by extracting local features from the object to produce an alarm in the case of the disappearance.
Currently, There are many E-commerce websites around the internet world. These E-commerce websites can be categorized into many types which one of them is C2C (Customer to Customer) websites such as eBay and Amazon. The main objective of C2C websites is an online market place that everyone can buy or sell anything at any time. Since, there are a lot of products in the E-commerce websites and each...
While, in stratosphere, high level ozone concentration protects the Earth against ultraviolet radiation, in lower troposphere it has negative effects on human health and environment. The goal of this study is to determine the feature groups that are related to abrupt changes in the level of ozone. Linear discriminant analysis and support vector machines methods are used to explore which combination...
Although there are many studies on computer-aided drug design in recent years, determination of proteins for drug candidates is a remarkable area for research. The first major shortcoming of this kind of problems is the feature selection representing the protein structure best, the former one is the computational complexity. We use three datasets with different sizes such as Cherkasov dataset with...
Since the Internet has become a huge repository of information, many studies address the issue of web pages categorization. For web page classification, we want to find a subset of words which help to discriminate between different kinds of web pages, so we introduced feature selection. In this paper, we study some feature selection methods such as ReliefF and Symmetrical Uncertainty. Also, the high...
In this article we propose a fusion model at data-level based on a linear combination of kernels. These kernels functions will be evaluated on disjoint entries, on the signature acquired from visible respective infrared spectrum. Therefore, we have to choose the proper numeric signature for the visible and for the infrared images. In order to retain just the best suited features, different feature...
Reduction of feature dimensionality is of considerable importance in machine learning. The generalization performance of classification system improves when correlated and redundant features are removed. In order to reduce the dimensionality of pattern representation, A new feature election method for support vector machine is proposed. Based on pattern similarity measurement in kernel space, lass...
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