Image pattern classification is considered a significant step for image and video processing.Although various image pattern algorithms have been proposed so far that achieved adequate classification,achieving higher accuracy while reducing the computation time remains challenging to date. A robust imagepattern classification method is essential to obtain the desired accuracy. This method can be accuratelyclassify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism.Moreover, to date, most of the existing studies are focused on evaluating their methods based on specificorthogonal moments, which limits the understanding of their potential application to various DiscreteOrthogonal Moments (DOMs). Therefore, finding a fast PET classification method that accurately clas-sify image pattern is crucial. To this end, this paper proposes a new scheme for accurate and fast imagepattern classification using an efficient DOM. To reduce the computational complexity of feature extraction,an election mechanism is proposed to reduce the number of processed block patterns. In addition, supportvector machine is used to classify the extracted features for different block patterns. The proposed scheme isevaluated by comparing the accuracy of the proposed method with the accuracy achieved by state-of-the-artmethods. In addition, we compare the performance of the proposed method based on different DOMs toget the robust one. The results show that the proposed method achieves the highest classification accuracycompared with the existing methods in all the scenarios considered
In this paper a method to determine whether an image is forged (spliced) or not is presented. The proposed method is based on a classification model to determine the authenticity of a tested image. Image splicing causes many sharp edges (high frequencies) and discontinuities to appear in the spliced image. Capturing these high frequencies in the wavelet domain rather than in the spatial domain is investigated in this paper. Correlation between high-frequency sub-bands coefficients of Discrete Wavelet Transform (DWT) is also described using co-occurrence matrix. This matrix was an input feature vector to a classifier. The best accuracy of 92.79% and 94.56% on Casia v1.0 and Casia v2.0 datasets respectively was achieved. This pe
... Show More
(1)
(6)
(2)
(15)
(13)
Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor
... Show MoreFace detection is one of the important applications of biometric technology and image processing. Convolutional neural networks (CNN) have been successfully used with great results in the areas of image processing as well as pattern recognition. In the recent years, deep learning techniques specifically CNN techniques have achieved marvellous accuracy rates on face detection field. Therefore, this study provides a comprehensive analysis of face detection research and applications that use various CNN methods and algorithms. This paper presents ten of the most recent studies and illustrate the achieved performance of each method.
(6)
(2)
(5)
(3)
(7)
(7)
Regarding the security of computer systems, the intrusion detection systems (IDSs) are essential components for the detection of attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in real time. A major drawback of the IDS is their inability to provide adequate sensitivity and accuracy, coupled with their failure in processing enormous data. The issue of classification time is greatly reduced with the IDS through feature selection. In this paper, a new feature selection algorithm based on Firefly Algorithm (FA) is proposed. In addition, the naïve bayesian classifier is used to discriminate attack behaviour from normal behaviour in the network tra
... Show MoreThe definition of orthogonal generalized higher k-derivation is examined in this paper and we introduced some of its related results.
In this paper a Г-ring M is presented. We will study the concept of orthogonal generalized symmetric higher bi-derivations on Г-ring. We prove that if M is a 2-torsion free semiprime Г-ring , and are orthogonal generalized symmetric higher bi-derivations associated with symmetric higher bi-derivations respectively for all n ϵN.