M. Imani

[ 1 ] - Feature extraction of hyperspectral images using boundary semi-labeled samples and hybrid criterion

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new fea...

[ 2 ] - Overlap-based feature weighting: The feature extraction of Hyperspectral remote sensing imagery

Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weigh...

[ 3 ] - Feature reduction of hyperspectral images: Discriminant analysis and the first principal component

When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. In this paper, we propose a supervised feature extraction method based on discriminant analysis (DA) which uses the first principal component (PC1) to weight the scatter matrices. The proposed method, called DA-PC1, copes with the small sample size problem and has...

[ 4 ] - Phishing website detection using weighted feature line embedding

The aim of phishing is tracing the users' s private information without their permission by designing a new website which mimics the trusted website. The specialists of information technology do not agree on a unique definition for the discriminative features that characterizes the phishing websites. Therefore, the number of reliable training samples in phishing detection problems is limited. M...

[ 5 ] - 3D Gabor Based Hyperspectral Anomaly Detection

Hyperspectral anomaly detection is one of the main challenging topics in both military and civilian fields. The spectral information contained in a hyperspectral cube provides a high ability for anomaly detection. In addition, the costly spatial information of adjacent pixels such as texture can also improve the discrimination between anomalous targets and background. Most studies miss the wort...

[ 6 ] - کاهش ابعاد داده‌های ابرطیفی به منظور افزایش جدایی‌پذیری کلاس‌ها و حفظ ساختار داده

Hyperspectral imaging with gathering hundreds spectral bands from the surface of the Earth allows us to separate materials with similar spectrum. Hyperspectral images can be used in many applications such as land chemical and physical parameter estimation, classification, target detection, unmixing, and so on. Among these applications, classification is especially interested. A hyperspectral im...

[ 7 ] - Supervised Feature Extraction of Face Images for Improvement of Recognition Accuracy

Dimensionality reduction methods transform or select a low dimensional feature space to efficiently represent the original high dimensional feature space of data. Feature reduction techniques are an important step in many pattern recognition problems in different fields especially in analyzing of high dimensional data. Hyperspectral images are acquired by remote sensors and human face images ar...

[ 8 ] - مطالعه نقش زنان در اقتصاد کشور انگلستان (اشتغال در جامعه و صنعت)

This article has no abstract.

[ 9 ] - Nonparametric Spectral-Spatial Anomaly Detection

Due to abundant spectral information contained in the hyperspectral images, they are suitable data for anomalous targets detection. The use of spatial features in addition to spectral ones can improve the anomaly detection performance. An anomaly detector, called nonparametric spectral-spatial detector (NSSD), is proposed in this work which utilizes the benefits of spatial features and local st...

[ 10 ] - Intrusion Detection in IOT based Networks Using Double Discriminant Analysis

Intrusion detection is one of the main challenges in wireless systems especially in Internet of things (IOT) based networks. There are various attack types such as probe, denial of service, remote to local and user to root. In addition to known attacks and malicious behaviors, there are various unknown attacks that some of them have similar behavior with respect to each other or mimic the norma...

[ 11 ] - Short Term Load Forecasting Using Empirical Mode Decomposition, Wavelet Transform and Support Vector Regression

The Short-term forecasting of electric load plays an important role in designing and operation of power systems. Due to the nature of the short-term electric load time series (nonlinear, non-constant, and non-seasonal), accurate prediction of the load is very challenging. In this article, a method for short-term daily and hourly load forecasting is proposed. In this method, in the first step, t...