With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusion Detection System (IDS). Success is measured by a variety of metrics, including accuracy, precision, recall, F1-Score, and execution time. Applying feature selection approaches such as Analysis of Variance (ANOVA), Mutual Information (MI), and Chi-Square (Ch-2) reduced execution time, increased detection efficiency and accuracy, and boosted overall performance. All classifiers achieve the greatest performance with 99.99% accuracy and the shortest computation time of 0.0089 seconds while using ANOVA with 10% of features.
The multi-focus image fusion method can fuse more than one focused image to generate a single image with more accurate description. The purpose of image fusion is to generate one image by combining information from many source images of the same scene. In this paper, a multi-focus image fusion method is proposed with a hybrid pixel level obtained in the spatial and transform domains. The proposed method is implemented on multi-focus source images in YCbCr color space. As the first step two-level stationary wavelet transform was applied on the Y channel of two source images. The fused Y channel is implemented by using many fusion rule techniques. The Cb and Cr channels of the source images are fused using principal component analysis (PCA).
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The game of football is one of the most popular games in the world because of its beauty in the hearts of its fans. From this position, the game of five football for people with simple mental disability has become as much attention to many other sporting events, so the researchers believe that the tests of basic skills match the level of individuals tested In terms of their age and mental ability, the technical aspects were adopted as a means of selecting those who are qualified to practice this game in the simplest form, so the importance of the research problem in designing and standardizing two dribbling skating tests for members of this category and It depends by training their cadres during the selection process. The research community
... Show MoreThe five-a- Side Soccer for people with a minor mental disability has become as important as many other sporting events. Therefore, the researcher considers that the basic skills tests are suited to the level of the tested individuals in terms of their age and mental ability. The technical aspects were adopted as a means of selecting from They are qualified to practice this game in the simplest form, so show the importance of the problem of research through the design and codification of two tests of handling skills belonging to this category and adopted by the training cadres during the selection process. The research community for people with minor mental disabilities determines the male category of the mental and social disability instit
... Show MoreKE Sharquie, AF Hameed, AA Noaimi, Indian Journal of Pathology and Microbiology, 2016 - Cited by 12
Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct
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