Cyber attacks have greatly increased over the years, where the attackers have progressively improved in devising attacks towards a specific target. To aid in identifying and defending against cyber attacks we propose a cyber attack taxonomy called AVOIDIT (Attack Vector, Operational Impact, Defense, Information Impact, and Target). We use five major classifiers to characterize the nature of an ...

OBJECTIVE To determine whether automated classifiers can be used for correctly identifying target categorization responses from averaged event-related potentials (ERPs) along with identifying appropriate features and classification models for computer-assisted investigation of attentional processes. METHODS ERPs were recorded during a target categorization task. Automated classification of av...

In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed. In multi-label classification, each of the input data sample belongs to one or more than one of the target labels. The traditional binary and multi-class classification where each sample belongs to only one target class forms the subset of multi-label class...

We study the problem of target identification from Synthetic Aperture Radar (SAR) imagery. Target classification using SAR imagery is a challenging problem due to large variations of target signature as the target aspect angle changes. Previous work on modeling wide angle SAR imagery has shown that point features, extracted from scattering center locations, result in a high dimensional feature ...

An approach for processing sonar signals with the ultimate goal of ocean bottom sediment classification and underwater buried target classification is presented in this paper. Work reported for sediment classification is based on sonar data collected by one of the AN/AQS-20’s sonars. Synthetic data, simulating data acquired by parametric sonar, is employed for target classification. The techniq...

Brain computer interaction (BCI) technologies have proven effective in utilizing single-trial classification algorithms to detect target images in rapid serial visualization presentation tasks. While many factors contribute to the accuracy of these algorithms, a critical aspect that is often overlooked concerns the feature similarity between target and non-target images. In most real-world envi...

Breast cancer has become a widespread disease around the world in young women. Expert systems, developed by data mining techniques, are valuable tools in diagnosis of breast cancer and can help physicians for decision making process. This paper presents a new hybrid data mining approach to classify two groups of breast cancer patients (malignant and benign). The proposed approach, AP-AMBFA, con...

In the literature, various approaches have been proposed to address the domain adaptation problem in sentiment classification (also called cross-domain sentiment classification). However, the adaptation performance normally much suffers when the data distributions in the source and target domains differ significantly. In this paper, we suggest to perform active learning for cross-domain sentime...