Comparative between optimization feature selection by using classifiers algorithms on spam email

Spam mail has become a rising phenomenon in a world that has recently witnessed high growth in the volume of emails. This indicates the need to develop an effective spam filter. At the present time, Classification algorithms for text mining are used for the classification of emails. This paper provides a description and evaluation of the effectiveness of three popular classifiers using optimization feature selections, such as Genetic algorithm, Harmony search, practical swarm optimization, and simulating annealing. The research focuses on a comparison of the effect of classifiers using K-nearest Neighbor (KNN), Naïve Bayesian (NB), and Support Vector Machine (SVM) on spam classifiers (without using feature selection) also enhances the reliability of feature selection by proposing optimization feature selection to reduce number of features that are not important

Hyperspectral Image Classification based on Dimensionality Reduction and Swarm Optimization

Hyperspectral images have high dimensions, making it difficult to determine accurate and efficient image segmentation algorithms. Dimension reduction data is done to overcome these problems. In this paper we use Discriminant independent component analysis (DICA). The accuracy and efficiency of the segmentation algorithm used will affect final results of image classification. In this paper a new method of multilevel thresholding is introduced for segmentation of hyperspectral images. A method of swarm optimization approach, namely Darwinian Particle Swarm Optimization (DPSO) is used to find n-1 optimal m-level threshold on a given image. A new classification image approach based on Darwinian particle swarm optimization (DPSO) and support vector machine (SVM) is used in this paper. The method introduced in this paper is compared to existing approach. The results showed that the proposed method was better than the standard SVM in terms of classification accuracy namely average accuracy (AA), overall accuracy (OA and Kappa index (K)

Image Aesthetic Assessment: A Comparative Study of Hand-Crafted & Deep Learning Models

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Non-local tensor completion for multitemporal remotely sensed images inpainting

Remotely sensed images may contain some missing areas because of poor weather conditions and sensor failure. Information of those areas may play an important role in the interpretation of multitemporal remotely sensed data. The paper aims at reconstructing the missing information by a non-local low-rank tensor completion method (NL-LRTC). First, nonlocal correlations in the spatial domain are taken into account by searching and grouping similar image patches in a large search window. Then low-rankness of the identified 4-order tensor groups is promoted to consider their correlations in spatial, spectral, and temporal domains, while reconstructing the underlying patterns. Experimental results on simulated and real data demonstrate that the proposed method is effective both qualitatively and quantitatively. In addition, the proposed method is computationally efficient compared to other patch based methods such as the recent proposed PM-MTGSR method