Plant identification has been a challenging task for many researchers. Several researches propose... more Plant identification has been a challenging task for many researchers. Several researches proposed various techniques for plant identification based on leaves shape. However, image segmentation is an essential and critical part of analyzing the leaves images. This paper, proposed an efficient plant species identification model using the digital images of leaves. The proposed identification model adopts the particle swarm optimization for leaves images segmentation. Then, feature selection process using information gain and discritization process are applied to the segmented image's features. The proposed model was evaluated on the Flavia dataset. Experimental results on different kind of classifiers show an improvement in the identification accuracy up to 98.7%
Plant identification is vital for the management of plant species. An automated plant identificat... more Plant identification is vital for the management of plant species. An automated plant identification system is required for the characterization of plant species without requiring the expertise of botanists. This paper presents an efficient and computational model for plant species identification using digital images of leaves. The proposed identification system combines the leaf biometric features, where shape and venation features are used for leaf image classification. 10 combined biometric leaf features are extracted and passed to Hidden naaive bays classifiers to be categorized. Several experiments are conducted and demonstrated on 1907 sample leaves of 32 different plant species taken form Flavia dataset. Where, the proposed plant identification model shows consistently performances of 97% average identification accuracy.
Advances in Intelligent Systems and Computing, 2013
ABSTRACT Intrusion detection systems (IDSs) is an essential key for network defense. Many classif... more ABSTRACT Intrusion detection systems (IDSs) is an essential key for network defense. Many classification algorithms have been proposed for the design of network IDS. Data preprocessing is a common phase to the classification learning algorithm, which leads to improve the network IDS performance. One of the important data preprocessing steps is discretization, where continuous features are converted into nominal ones. This paper addresses the impact of applying discretization on building network IDS. Furthermore, it explores the impact of the quality of the classification algorithms when combining discretization with genetic algorithm (GA) as a feature selection method for network IDS. In order to evaluate the performance of the introduced network IDS, several classifiers algorithms; rules based classifiers (Ridor, Decision table), trees classifiers (REPTree, C 4.5, Random Forest) and Na¨ıve bays classifier are used. Several groups of experiments are conducted and demonstrated on the NSL-KDD dataset. Experiments show that discretization has a positive influence on the time to classify the test instances. Which is an important factor if real time network IDS is desired.
Communications in Computer and Information Science, 2011
Feature selection is a preprocessing step to machine learning, used to reduce the dimensionality ... more Feature selection is a preprocessing step to machine learning, used to reduce the dimensionality of the dataset by removing irrelevant data. Variety of feature selection methods have been developed in the literature in order to increas the learning accuracy and reduce its complexity. In this paper we proposed a Bi-Layer behavioral-based feature selection approach. The proposed approach consists of two layers, in the first layer information gain is used to rank the features and select a new set of features depending ...
Communications in Computer and Information Science, 2013
ABSTRACT Feature selection is a preprocessing phase to machine learning, which leads to increase ... more ABSTRACT Feature selection is a preprocessing phase to machine learning, which leads to increase the classification accuracy and reduce its complexity. However, the increase of data dimensionality poses a challenge to many existing feature selection methods. This paper formulates and validates a method for selecting optimal feature subset based on the analysis of the Pearson correlation coefficients. We adopt the correlation analysis between two variables as a feature goodness measure. Where, a feature is good if it is highly correlated to the class and is low correlated to the other features. To evaluate the proposed Feature selection method, experiments are applied on NSL-KDD dataset. The experiments shows that, the number of features is reduced from 41 to 17 features, which leads to improve the classification accuracy to 99.1%. Also,The efficiency of the proposed linear correlation feature selection method is demonstrated through extensive comparisons with other well known feature selection methods.
Intrusion detection systems (IDSs) is an essential key for network defense. The hybrid intrusion ... more Intrusion detection systems (IDSs) is an essential key for network defense. The hybrid intrusion detection system combines the individual base classifiers and feature selection algorithm to maximize detection accuracy and minimize computational complexity. We investigated the performance of Genetic algorithm-based feature selection system to reduce the data features space and then the hidden naïve bays (HNB) system were adapted to classify the network intrusion into five outcomes: normal, and four anomaly types including ...
Plant identification has been a challenging task for many researchers. Several researches propose... more Plant identification has been a challenging task for many researchers. Several researches proposed various techniques for plant identification based on leaves shape. However, image segmentation is an essential and critical part of analyzing the leaves images. This paper, proposed an efficient plant species identification model using the digital images of leaves. The proposed identification model adopts the particle swarm optimization for leaves images segmentation. Then, feature selection process using information gain and discritization process are applied to the segmented image's features. The proposed model was evaluated on the Flavia dataset. Experimental results on different kind of classifiers show an improvement in the identification accuracy up to 98.7%
Plant identification is vital for the management of plant species. An automated plant identificat... more Plant identification is vital for the management of plant species. An automated plant identification system is required for the characterization of plant species without requiring the expertise of botanists. This paper presents an efficient and computational model for plant species identification using digital images of leaves. The proposed identification system combines the leaf biometric features, where shape and venation features are used for leaf image classification. 10 combined biometric leaf features are extracted and passed to Hidden naaive bays classifiers to be categorized. Several experiments are conducted and demonstrated on 1907 sample leaves of 32 different plant species taken form Flavia dataset. Where, the proposed plant identification model shows consistently performances of 97% average identification accuracy.
Advances in Intelligent Systems and Computing, 2013
ABSTRACT Intrusion detection systems (IDSs) is an essential key for network defense. Many classif... more ABSTRACT Intrusion detection systems (IDSs) is an essential key for network defense. Many classification algorithms have been proposed for the design of network IDS. Data preprocessing is a common phase to the classification learning algorithm, which leads to improve the network IDS performance. One of the important data preprocessing steps is discretization, where continuous features are converted into nominal ones. This paper addresses the impact of applying discretization on building network IDS. Furthermore, it explores the impact of the quality of the classification algorithms when combining discretization with genetic algorithm (GA) as a feature selection method for network IDS. In order to evaluate the performance of the introduced network IDS, several classifiers algorithms; rules based classifiers (Ridor, Decision table), trees classifiers (REPTree, C 4.5, Random Forest) and Na¨ıve bays classifier are used. Several groups of experiments are conducted and demonstrated on the NSL-KDD dataset. Experiments show that discretization has a positive influence on the time to classify the test instances. Which is an important factor if real time network IDS is desired.
Communications in Computer and Information Science, 2011
Feature selection is a preprocessing step to machine learning, used to reduce the dimensionality ... more Feature selection is a preprocessing step to machine learning, used to reduce the dimensionality of the dataset by removing irrelevant data. Variety of feature selection methods have been developed in the literature in order to increas the learning accuracy and reduce its complexity. In this paper we proposed a Bi-Layer behavioral-based feature selection approach. The proposed approach consists of two layers, in the first layer information gain is used to rank the features and select a new set of features depending ...
Communications in Computer and Information Science, 2013
ABSTRACT Feature selection is a preprocessing phase to machine learning, which leads to increase ... more ABSTRACT Feature selection is a preprocessing phase to machine learning, which leads to increase the classification accuracy and reduce its complexity. However, the increase of data dimensionality poses a challenge to many existing feature selection methods. This paper formulates and validates a method for selecting optimal feature subset based on the analysis of the Pearson correlation coefficients. We adopt the correlation analysis between two variables as a feature goodness measure. Where, a feature is good if it is highly correlated to the class and is low correlated to the other features. To evaluate the proposed Feature selection method, experiments are applied on NSL-KDD dataset. The experiments shows that, the number of features is reduced from 41 to 17 features, which leads to improve the classification accuracy to 99.1%. Also,The efficiency of the proposed linear correlation feature selection method is demonstrated through extensive comparisons with other well known feature selection methods.
Intrusion detection systems (IDSs) is an essential key for network defense. The hybrid intrusion ... more Intrusion detection systems (IDSs) is an essential key for network defense. The hybrid intrusion detection system combines the individual base classifiers and feature selection algorithm to maximize detection accuracy and minimize computational complexity. We investigated the performance of Genetic algorithm-based feature selection system to reduce the data features space and then the hidden naïve bays (HNB) system were adapted to classify the network intrusion into five outcomes: normal, and four anomaly types including ...
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