Maryam Imani

Bio: Maryam Imani is an academic researcher from Tarbiat Modares University. The author has contributed to research in topics: Feature extraction & Hyperspectral imaging. The author has an hindex of 15, co-authored 96 publications receiving 796 citations. Previous affiliations of Maryam Imani include Shahed University.

An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges

Abstract: Hyperspectral images (HSIs) have a cube form containing spatial information in two dimensions and rich spectral information in the third one. The high volume of spectral bands allows discrimination between various materials with high details. Moreover, by utilizing the spatial features of image such as shape, texture and geometrical structures, the land cover discrimination will be improved. So, fusion of spectral and spatial information can significantly improve the HSI classification. In this work, the spectral-spatial information fusion methods are categorized into three main groups. The first group contains segmentation based methods where objects or super-pixels are used instead of pixels for classification or the obtained segmentation map is used for relaxation of the pixel-wise classification map. The second group consists of feature fusion methods which are divided into six sub-groups: features stacking, joint spectral-spatial feature extraction, kernel based classifiers, representation based classifiers, 3D spectral-spatial feature extraction and deep learning based classifiers. The third fusion methods are decision fusion based approaches where complementary information of several classifiers are contributed for achieving the final classification map. A review of different methods in each category, is presented. Moreover, the advantages and difficulties/disadvantages of each group are discussed. The performance of various fusion methods are assessed in terms of classification accuracy and running time using experiments on three popular hyperspectral images. The results show that the feature fusion methods although are time consuming but can provide superior classification accuracy compared to other methods. Study of this work can be very useful for all researchers interested in HSI feature extraction, fusion and classification.

Classification of heart sound signal using curve fitting and fractal dimension

Abstract: Cardiovascular disease is one of the major causes of mortality worldwide. Audio signal produced by the mechanical activity of heart provides useful information about the heart valves operation. To increase discriminability between heart sound signals of different normal and abnormal persons, extraction of appropriate features is so important. An accurate segmentation of heart sound signal requires its corresponding ECG 1 signal. But, acquiring of ECG is generally expensive and time consuming. So, one of the main goals of this paper is to eliminate the segmentation step. In this paper, two feature extraction methods are proposed. In the first proposed method, curve fitting is used to achieve the information contained in the sequence of heart sound signal. In the second method, the powerful features extracted by MFCC 2 are fused with the fractal features by stacking. The experiments are done on six popular datasets to assess the efficiency of different methods One of the data sets contains four classes and the rest of them include two classes (normal and pathologic). In the classification step, the nearest neighbor classifier with Euclidean distance is used. The proposed method has good performance compared to previous methods such as Filter banks and Wavelet transform. Particularly, the performance of the second method is significantly better than the first proposed method. For three data sets, the overall accuracy of 92%, 81% and 98% are achieved, respectively.

Convolutional and recurrent neural network based model for short-term load forecasting

Abstract: The consumed electrical load is affected by many external factors such as weather, season of the year, weekday or weekend and holiday. In this paper, it is tried to provide a high accurate forecasting model for hourly load consumption with considering these external variables. At first, the electrical load and temperature time series are rearranged into separate two-dimensional matrices. Convolutional neural networks (CNNs) are utilized to extract the load and temperature features. The autocorrelation coefficients of the load and temperature sequences are used to determine the kernel size of the convolutional layers. At this stage, the convolutional layers specifically convert the univariate data to multidimensional features by applying two-dimensional convolutional kernels, which potentially increase the forecasting capability of recurrent neural networks. On the other hand, long short term memory (LSTM) and gated recurrent unit (GRU) are able to hold short-term and long-term memories. Therefore, in the next stage, the multidimensional features extracted by 2-D CNNs are fed as input to the bidirectional propagating GRU and LSTM units to perform hourly electrical load forecasting. The results of experiments on two datasets show the superiority of the proposed method compared to some recent works in the field of short-term load forecasting.

Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples

Abstract: Feature extraction plays a central role in classification of hyperspectral data. We propose a clustering-based feature extraction (CBFE) method in this letter. The proposed method is supervised and only needs to calculate the first-order statistics. Thus, CBFE has better performance than some popular supervised feature extraction methods such as linear discriminant analysis, generalized discriminant analysis, and nonparametric weighted feature extraction in small sample size situation. In addition, CBFE works better than unsupervised approaches such as principal component analysis in classification applications. CBFE considers a vector associated with each band that is composed by the mean values of all classes in that band. Then, a clustering method such as k-means is run to group the similar bands in one cluster. The selected number of clusters is equal to the number of extracted features. Experiments carried out on two different hyperspectral data sets demonstrate that the CBFE has better performance in comparison with some conventional feature extraction methods.

A survey of emotion recognition methods with emphasis on E-Learning environments

Abstract: Emotions play an important role in the learning process. Considering the learner's emotions is essential for electronic learning (e-learning) systems. Some researchers have proposed that system should induce and conduct the learner's emotions to the suitable state. But, at first, the learner's emotions have to be recognized by the system. There are different methods in the context of human emotions recognition. The emotions can be recognized by asking from the user, tracking implicit parameters, voice recognition, facial expression recognition, vital signals and gesture recognition. Moreover, hybrid methods have been also proposed which use two or more of these methods through fusing multi-modal emotional cues. In the e-learning systems, the system's user is the learner. For some reasons, which have been discussed in this study, some of the user emotions recognition methods are more suitable in the e-learning systems and some of them are inappropriate. In this work, different emotion theories are reviewed. Then, various emotions recognition methods have been represented and their advantages and disadvantages of them have been discussed for utilizing in the e-learning systems. According to the findings of this research, the multi-modal emotion recognition systems through information fusion as facial expressions, body gestures and user's messages provide better efficiency than the single-modal ones.

The Analysis of Time Series: An Introduction

Abstract: The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

Geometric representation of high dimension, low sample size data

Abstract: High dimension, low sample size data are emerging in various areas of science. We find a common structure underlying many such data sets by using a non-standard type of asymptotics: the dimension tends to ∞ while the sample size is fixed. Our analysis shows a tendency for the data to lie deterministically at the vertices of a regular simplex. Essentially all the randomness in the data appears only as a random rotation of this simplex. This geometric representation is used to obtain several new statistical insights. Copyright 2005 Royal Statistical Society.