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This article provides a comparison of two feature selection algorithms, Information Gain Thresholding and Koller and Sahami's algorithm in the context of text document classification on the Reuters Corpus Volume 1 dataset. The algorithms were evaluated by testing the performance of classifiers trained on the features they select from a given dataset. Results show that Koller and Sahami's algorithm...
Identifying families of malware is today considered a fundamental problem in the context of computer security. The correct mapping of a malicious sample to a known family simplifies its analysis and allows experts to focus their efforts only on those samples presenting unknown characteristics or behaviours, thus improving the efficiency of the malware analysis process. Grouping malware in families...
Grey Wolf Optimizer (GWO) is a new meta-heuristic optimization. It is inspired by the unique predator strategy and organization system of grey wolves. Since the GWO algorithm is easy to fall into local optimum especially when it is used in the high-dimensional data, an improved GWO algorithm combined with Cuckoo Search (CS) is proposed in this paper. By introducing the global-search ability of CS...
Automatic malware categorization plays an important role in combating the current large volume of malware and aiding the corresponding forensics. Generally, there are lot of sample information could be extracted with the static tools and dynamic sandbox for malware analysis. Combine these obtained features effectively for further analysis would provides us a better understanding. On the other hand,...
the division of the test paper can reflect the quality of examination paper, but it is difficult to find some decisive courses in dozens of courses. In order to find out the curriculum that decides the role of different levels of students, the concept of course discrimination is proposed, which focuses on the value of course discrimination, the classification method and the proportion of special courses...
Cross domain data such as numerical or categorical types are ubiquitous in practical network. Network anomaly detection based on cluster analysis exist some difficulties, for example, the initial center of cluster analysis is sensitive and easy to fall into the local optimal solution. Cross domain data involved great information, but can't be effectively used, which will influence the performance...
Theories inspired by the working and the structure of the human brain have been applied in various problems in computer vision, such as Artificial Neural Networks (ANNs). The concept of receptive and inhibitory fields have been adopted with success to improve the capability of the ANNs and are present in the deep learning models, such as ConvNet and LIPNet, that have been successfully used in many...
The high-probability ictus of cardiovascular diseases as well as the rapid proliferation of personal wearable devices points to the emergence of machine aided automatic identification and diagnosis with electrocardiograph (ECG). Most of existing algorithms of the identification of normal/abnormal ECG waveforms are computationally complicated and time-consuming, which cannot couple with the constant...
A algorithm of fingerprint image segmentation is the essence of the whole fingerprint system, and this paper proposes an improved one which based on adaptive dynamic threshold segmentation algorithm. It ameliorates Otsu algorithm according to the unevenness of grey scale. This algorithm distinguishes the target from the background in the image with the combination of threshold segmentation and region...
In this paper, real-time recognition and tracking of multiple similar targets at 6-DOF motion is studied. A real-time multi-target recognition algorithm is proposed and implemented based on Marker to solve the difficult problem of distinguishing multiple similar targets. Because the lighting conditions of markers at 6-DOF motion are widely changeable, existing marker recognition algorithms are sensitive...
Over the last two decades, different differential evolution (DE) variants have been successfully used to solve different optimization problems. However, no single DE algorithm has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple DE operators in a single framework. However, utilizing a problem's landscape in the design...
In the past decade, online music streaming services (MSS), e.g., Pandora and Spotify, revolutionized the way people access, consume and share music. MSS serve users with a huge digital music library, various kinds of music discovery channels, and a number of tools for music sharing and management (e.g. bookmark, playlist, comment, etc.). As a result, metadata and user-generated data hosted on MSS...
In the traditional abandoned object detection(AOD for short) researches, the monitoring equipment is stationary which is limited in practice. In this paper, a novel abandoned object detection based on tachograph is proposed. Firstly, we use the Harris-SIFT features and particle filter to achieve object tracking. After that, the VIBE algorithm is applied to detect the abandoned object with background...
The extraction of multi-view matching points is one of the key elements in 3D reconstruction of multi-view image scene, because the extraction results will directly affect the accuracy of 3D reconstruction. With the conversion from the extraction of multi-view matching points to dynamic connectivity, a solution based on the Union Find algorithm was designed. The efficient tree structure with parent-link...
This paper introduces two novel algorithms for detecting groups of people standing or freely moving in a crowded environment. The proposed algorithms exploit low-level features extracted from videos. The first algorithm, the Link Method, uses a learning and forgetting strategy for modeling dynamics of proxemics between individuals. Two versions of this algorithm are proposed: they differ in the analysis...
Network-based malware classification plays an important role in improving system security than system-based malware classification. The vast majority of malware needs a network activity in order to accomplish its purpose (e.g., downloading malware, connecting to a C&C server, etc.). Many malware classification approaches based on network behavior have thus been proposed. Nevertheless, they merely...
Recently, the number of features in different problem domains has grown enormously. In order to select the best representation (attributes) for these problems, a deep knowledge of the problem domain is required. As this type of knowledge is not always possible, feature selection needs to be applied as an automatic selection process of the most relevant attributes in a dataset. In this paper, we propose...
Anomaly detection technique play an extraordinary role in the Intrusion Detection System (IDS) for its ability to detect novel attacks. To overcome the high-dimensionality problem the anomaly detection cursed of, we propose a novel Meta-Heuristic-based Sequential Forward Selection (MH_SFS) feature selection algorithm, which can be generally implemented in anomaly detection system. It is an improvement...
This paper studies the content-based music retrieval and the methods for main modules. It improves the defect of DTW algorithm, which needs longer running time for melody matching, and proposes a DTW melody matching algorithm based on numerical index. It first establishes numerical index according to pitch difference information in the melody feature library. Then it performs rough matching based...
For any real-time application, detection and tracking of features becomes very important. The detection and tracking algorithms have to be very robust and efficient with least or zero false positives and false negatives. We use a novel combination for detection and tracking purpose. In this paper we propose a robust mouth region extraction and tracking algorithm that works in real-time. The region...
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