In recent years, the increased use of wireless networks for the transmission of large volumes of information has generated a myriad of security threats and privacy concerns; consequently, there has been the development of a number of preventive and protective measures including intrusion detection systems (IDS). Intrusion detection mechanisms play a pivotal role in securing computer and network systems; however, for various IDS, the performance remains a major issue. Moreover, the accuracy of existing methodologies for IDS using machine learning is heavily affected when the feature space grows. In this paper, we propose a IDS based on deep learning using feed forward deep neural networks (FFDNNs) coupled with a filter-based feature selection algorithm. The FFDNN-IDS is evaluated using the well-known NSL-knowledge discovery and data mining (NSL-KDD) dataset and it is compared to the following existing machine learning methods: support vectors machines, decision tree, K-Nearest Neighbor, and Naive Bayes. The experimental results prove that the FFDNN-IDS achieves an increase in accuracy in comparison to other methods.

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A Deep Learning Method With Filter Based Feature Engineering for Wireless Intrusion Detection System

Fractional Charlier moments for image reconstruction and image watermarking

Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques

Measuring and improving adaptive capacity in resilient systems by means of an integrated DEA-Machine learning approach.

Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task for two main reasons: lack of sufficient training data for every class and difficulty in learning discriminative features for representation. In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i.e., zero-shot fine-grained classification. In the first feature learning phase, we finetune deep convolutional neural networks using hierarchical semantic structure among fine-grained classes to extract discriminative deep visual features. Meanwhile, a domain adaptation structure is induced into deep convolutional neural networks to avoid domain shift from training data to test data. In the second label inference phase, a semantic directed graph is constructed over attributes of fine-grained classes. Based on this graph, we develop a label propagation algorithm to infer the labels of images in the unseen classes. Experimental results on two benchmark datasets demonstrate that our model outperforms the state-of-the-art zero-shot learning models. In addition, the features obtained by our feature learning model also yield significant gains when they are used by other zero-shot learning models, which shows the flexility of our model in zero-shot fine-grained classification.

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Zero-shot Fine-grained Classification by Deep Feature Learning with Semantics

Classification and Recognition of 3D Image of Charlier moments using a Multilayer Perceptron Architecture

Recently, orthogonal moments have become efficient tools for two-dimensional and three-dimensional (2D and 3D) image not only in pattern recognition, image vision, but also in image processing and applications engineering. Yet, there is still a major difficulty in 3D rotation invariants. In this paper, we propose new sets of invariants for 2D and 3D rotation, scaling and translation based on orthogonal radial Hahn moments. We also present theoretical mathematics to derive them. Thus, this paper introduces in the first case new 2D radial Hahn moments based on polar representation of an object by one-dimensional orthogonal discrete Hahn polynomials, and a circular function. In the second case, we present new 3D radial Hahn moments using a spherical representation of volumetric image by one-dimensional orthogonal discrete Hahn polynomials and a spherical function. Further 2D and 3D invariants are derived from the proposed 2D and 3D radial Hahn moments respectively, which appear as the third case. In order to test the proposed approach, we have resolved three issues:the image reconstruction, the invariance of rotation, scaling and translation, and the pattern recognition. The result of experiments show that the Hahn moments have done better than the Krawtchouk moments, with and without noise. Simultaneously, the mentioned reconstruction converges quickly to the original image using 2D and 3D radial Hahn moments, and the test images are clearly recognized from a set of images that are available in COIL-20 database for 2D image, and Princeton shape benchmark (PSB) database for 3D image.

Radial Hahn Moment Invariants for 2D and 3D Image Recognition

In this work, we propose a new set of an intelligent classifier of biomedical color images using Quaternion Discrete Radial Tchebichef Moments (QRTM). This moment has shown very high robustness in terms of color image representation. Thus, we propose a new approach for the fast and accurate reconstruction of multi-level and color images. This approach based on the reconstruction of color images by applying matrix computation. In the second step, we propose a new method for the extraction of Quaternion Discrete Radial Tchebichef Moments invariant (QRTMI). This method based on the representation of the extraction of these invariants. The performance of the invariance of these moments under the three types of geometrical transformations (Rotation, translation, scale) are very important. Finally, we present a new model based on a Multilayer perceptron (MLP) for the classification of biomedical color images. The experimental results show that the QRTMI very powerful compared with radial Legendre - Fourier and Quaternion discrete radial Krawtchouk moments invariant for the biomedical colors images using the BreaKHis Database (Breast Tumor).

Artificial intelligent classification of biomedical color image using quaternion discrete radial Tchebichef moments

This paper conducts a study to identify pedagogical approaches and gameplay techniques involved in the development of serious games for teaching scientific courses in general especially programming languages. The concept of serious games is increasingly popular and is considered as an innovative teaching practice since it is based on information and communication technology and gamification to foster learning. To this end, a serious game "Perobo" will be introduced and discussed. It is based on a set of gameplay techniques and pedagogical approaches used for teaching pointers, considered as a difficult concept in C programming language, and essential for programming complex and advanced programs. The game is also based on a taxonomy design to define the learning levels.

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A Serious Game for Learning C Programming Language Concepts Using Solo Taxonomy

Radial Charlier moments as discrete orthogonal moments in the polar coordinate are better descriptor in image processing applications and pattern recognition. However, the translation and scale invariant property of these moments have not been studied due to its complexity of the problem. In this paper, we present a method to construct a set of rotation invariants extracted from radial Charlier moments, named radial Charlier moment invariants (RCMI). Experimental results show the efficiency and the robustness to reconstruction error (MSE), peak signal to noise ratio (PSNR) of the proposed method.

Ahmed Tahiri

Papers

Classification and Recognition of 3D Image of Charlier moments using a Multilayer Perceptron Architecture

Radial Hahn Moment Invariants for 2D and 3D Image Recognition

Artificial intelligent classification of biomedical color image using quaternion discrete radial Tchebichef moments

A Serious Game for Learning C Programming Language Concepts Using Solo Taxonomy

Radial Charlier moment invariants for 2D object/image recognition