Intrusion detection is a crucial part for security of information systems. Most intrusion detection systems use all features in their databases while some of these features may be irrelevant or redundant and they do not contribute to the process of intrusion detection. Therefore, different feature ranking and feature selection techniques are proposed. In this paper, hybrid feature selection methods are used to select and rank reliable features and eliminate irrelevant and useless features to have a more accurate and reliable intrusion detection process. Due to the low cost and low accuracy of filtering methods, a combination of these methods could possibly improve their accuracy by a reasonable cost and create a balance between them. In the first phase, two subsets of reliable features are created by application of information gain and symmetrical uncertainty filtering methods. In the second phase, the two subsets are merged, weighted and ranked to extract the most important features. This feature ranking which is done by the combination of two filtering methods, leads to higher the accuracy of intrusion detection. KDD99 standard dataset for intrusion detection is used for experiments. The better detection rate obtained in proposed method is shown by comparing it with other feature selection methods that are applied on the same dataset.

Classification algorithms are unable to make reliable models on the datasets with huge sizes. These datasets contain many irrelevant and redundant features that mislead the classifiers. Furthermore, many huge datasets have imbalanced class distribution which leads to bias over majority class in the classification process. In this paper combination of unsupervised dimensionality reduction methods with resampling is proposed and the results are tested on LungCancer dataset. In the first step PCA is applied on LungCancer dataset to compact the dataset and eliminate irrelevant features and in the second step SMOTE resampling is carried out to balance the class distribution and increase the variety of sample domain. Finally, Naïve Bayes classifier is applied on the resulting dataset and the results are compared and evaluation metrics are calculated. The experiments show the effectiveness of the proposed method across four evaluation metrics: Overall accuracy, False Positive Rate, Precision, Recall.

Introduction:Methamphetamine is a powerful psychostimulant that causes significant neurological impairments with long-lasting effects and has provoked serious international concerns about public health. Denial of drug abuse and drug craving are two important factors that make the diagnosis and treatment extremely challenging. Here, we present a novel and rapid noninvasive method with potential application for differentiation and monitoring methamphetamine abuse.Methods:Visual stimuli comprised a series of images with neutral and methamphetamine-related content. A total of 10 methamphetamine abusers and 10 age-gender matched controls participated in the experiments. Event-related potentials (ERPs) were recorded and compared using a time window analysis method. The ERPs were divided into 19 time windows of 100 ms with 50 ms overlaps. The area of positive sections below each window was calculated to measure the differences between the two groups.Results:Significant differences between two groups were observed from 250 to 500 ms (P300) in response to methamphetamine-related visual stimuli and 600 to 800 ms in response to neutral stimuli.Conclusion:This study presented a novel and noninvasive method based on neural correlates to discriminate healthy individuals from methamphetamine drug abusers. This method can be employed in treatment and monitoring of the methamphetamine abuse.

This paper presents a design and fabrication of an ultra-wideband bandpass filter with two notched (rejection) bands. Ultra-wideband (UWB) systems are systems with the electromagnetic spectrum from 3.1 GHz to 10.6 GHz. The designed filter removes WLAN and satellite signals which are 5.8 GHz and 8 GHz. For designing filter, we use a stepped-impedance stub-loaded resonator (SISLR). To provide two notched bands, a radial stub loaded resonator with a defected microstrip structure (DMS) is used. The presented filter has more analytic relations and simpler structure than prior works. This filter is fabricated on an RO4003 substrate with dielectric constant of 3.55. The dimensions of the filter are 10 × 25 mm 2 which are more compact than prior structures. The measurements have a good agreement with predicted results which verifies the feasibility of the UWB filter.

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