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Using Clustering algorithm to improve the effectiveness of test case prioritization has been well recognized by many researchers. Software fault prediction has been one of the active parts of software engineering, but to date, there are few test cases prioritization technique using fault prediction. We conjecture that if the code has a fault-proneness, the test cases covering the code will findfault...
Using Clustering algorithm to improve the effectiveness of test case prioritization has been well recognized by many researchers. Software fault prediction has been one of the active parts of software engineering, but to date, there are few test cases prioritization technique using fault prediction. We conjecture that if the code has a fault-proneness, the test cases covering the code will find fault...
This paper focuses on designing an Intrusion Detection System(IDS), which detects the family of attack in a dataset. An IDS detects various types of malicious traffic and computer usage which cannot be detected by a conventional firewall. In this proposed work, the data is extracted from UNSW_NB15 dataset. To identify the data cluster centers, the k means algorithm is used. A new and one dimensional...
In this paper, a blind bandwidth extension algorithm for music signals has been proposed. This method applies the K-means algorithm to firstly cluster audio data in the feature space, and constructs multiple envelope predictors for each cluster accordingly using Support Vector Regression (SVR). A set of well-established audio features for Music Information Retrieval (MIR) has been used to characterize...
Millions of file uploads and downloads happen every minute resulting in big data creation and manual text categorization is not possible. Hence, there is a need for automatic categorization of documents that makes storage and retrieval more efficient. This research paper proposes a hybrid text categorization model that combines both Rocchio algorithm and Random Forest algorithm to perform Multi-label...
Network traffic classification has been always an important technology of network management for a long time. In recent years, the more unknown P2P traffic flows on the Internet new applications produce, the more network bandwidth the traffic flows of P2P network applications will fully occupy. In order to preserve the quality of network service, it is very necessary to classify the P2P traffic flows...
Scene recognition applications on mobile devices receive increasing attentions in recent years. Due to mobile users' real-time requirement, an accurate and efficient scene recognition system is urgent for mobile applications. In this paper, we propose a novel discriminative codeword selection method by using the ensemble extreme learning machine (ELM) algorithm for fast and accurate scene recognition...
In large population speaker identification (SI) system, likelihood computations during testing stage can be time-consuming. In such a case, clustering method is applied to this situation. But the traditional clustering algorithm based on K-means is sensitive to the randomly chosen initial cluster centers. To address this issue, the paper proposes an improved clustering algorithm which uses an initial...
Computer vision based road detection is an indispensable and challenging task in many real-world applications such as obstacle detection in autonomous driving. Low-level image features (e.g., color and texture) and pre-trained models are commonly used for this task. In this paper, we propose a simple yet effective approach to detect roads from a single image, which avoids the supervised model training...
Condition monitoring and fault diagnosis of rotating machinery are very significant and practically challenging fields in industries for reducing maintenance costs. Fault diagnosis may be interpreted as a classification problem; therefore artificial intelligence-based classifiers can be efficiently used to classify normal and faulty machine conditions. K-means clustering is one of the methods applied...
In this paper, we study the problem of ‘test-driving’ a detector, i.e. allowing a human user to get a quick sense of how well the detector generalizes to their specific requirement. To this end, we present the first system that estimates detector performance interactively without extensive ground truthing using a human in the loop. We approach this as a problem of estimating proportions and show that...
Neural spikes define the human brain function. An accurate extraction of spike features leads to better understanding of brain functionality. The main challenge of feature extraction is to mitigate the effect of strong background noises. To address this problem, we introduce a new feature representation for neural spikes based on Cepstrum of multichannel recordings. Simulation results indicated that...
In order to detect image spam effectively, we propose a method based on a K-labels propagation model (KLPM) in this paper. Specifically, the speeded up robust features (SURF) of each image are extracted firstly. Then to standardize the features of each image, an improved means clustering algorithm is used to cluster these features and get the information of M cluster centers. Finally, after being...
Network security and intrusion detection are important in the modern world where communication happens via information networks. Traditional signature-based intrusion detection methods cannot find previously unknown attacks. On the other hand, algorithms used for anomaly detection often have black box qualities that are difficult to understand for people who are not algorithm experts. Rule extraction...
Texture classification algorithms were employed widely in analyzing remote sensing images, medical images, and industrial images testing and so on. There are many texture classification methods have been developed which including K-Vive based algorithms. Among of them, K-view-voting method achieved best performance by comparing with other K-view based methods. In this paper, after analyzing K-view-voting...
Due to increased size and complexity of software, software maintenance has become a very difficult task for developers, especially on bug fixing. For famous open source systems, the number of daily submitted bug reports is very high. Unfortunately, as most of bug reports were not assigned to appropriate developers for fixing related bugs, these bug reports need to be reassigned. A larger number of...
Most image understanding algorithms begin with the extraction of information thought to be relevant to the particular task. This is commonly known as feature extraction and has, up to this date, been a largely manual process, where a reasonable method is chosen through validation on the experimented dataset. In this work we propose a data driven, local histogram based feature extraction method that...
The traditional KNN algorithm for text classification has some insufficiencies, an improved KNN algorithm has been presented in this paper. By use of the clustering center vector, we put the distance of the be classified text and the text category into the similarity calculation formula, and take the ratio of the number of common features appear in two texts and the maximum number of respective features...
Texture images can be characterized with key features extracted from images. In this paper, the scale invariant feature transform (hereinafter SIFT) algorithm is utilized to generate local features for texture image classification. The local features are selected as inputs for texture classification framework. For each texture category, a texton dictionary is built based on the local features. To...
To defend a network system from security risks, intrusion detection systems (IDSs) have been playing an important role in recent years. There are two types of detection algorithms of IDSs: misuse detection and anomaly detection. Because misuse detection is based on a signature which is created from the features of attack traffic by security experts, it can achieve accurate and stable detection. However,...
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