A network (or formally a graph) can be described by a set of nodes and a set of edges connecting these nodes. Networks model many real-world phenomena in various research domains, such as biology, engineering and sociology. Community mining is discovering the groups in a network where individuals group of membership are not explicitly given. Detecting natural divisions in such complex networks is proved to be extremely NP-hard problem that recently enjoyed a considerable interest. Among the proposed methods, the field of evolutionary algorithms (EAs) takes a remarkable interest. To this end, the aim of this paper is to present the general statement of community detection problem in social networks. Then, it visits the problem as an optimization problem where a modularity-based ( ) and normalized mutual information ( ) metrics are formulated to describe the problem. An evolutionary algorithm is then expressed in the light of its characteristic components to tackle the problem. The presentation will highlight the possible alternative that can be adopted in this study for individual representation, fitness evaluations, and crossover and mutation operators. The results point out that adopting as a fitness function carries out more correct solutions than adopting the modularity function . Moreover, the strength of mutation has a background role. When coupled with non elite selection, increasing mutation probability could results in better solutions. However, when elitism is used, increasing mutation probability could bewilder the behavior of EA.
A network (or formally a graph) can be described by a set of nodes and a set of edges connecting these nodes. Networks model many real-world phenomena in various research domains, such as biology, engineering and sociology. Community mining is discovering the groups in a network where individuals group of membership are not explicitly given. Detecting natural divisions in such complex networks is proved to be extremely NP-hard problem that recently enjoyed a considerable interest. Among the proposed methods, the field of evolutionary algorithms (EAs) takes a remarkable interest. To this end, the aim of this paper is to present the general statement of community detection problem in social networks. Then, it visits the problem as an optimization problem where a modularity-based ( ) and normalized mutual information ( ) metrics are formulated to describe the problem. An evolutionary algorithm is then expressed in the light of its characteristic components to tackle the problem. The presentation will highlight the possible alternative that can be adopted in this study for individual representation, fitness evaluations, and crossover and mutation operators. The results point out that adopting as a fitness function carries out more correct solutions than adopting the modularity function . Moreover, the strength of mutation has a background role. When coupled with non elite selection, increasing mutation probability could results in better solutions. However, when elitism is used, increasing mutation probability could bewilder the behavior of EA.
Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead
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The current study has sought to identify the levels of the compound thinking among secondary school students basing and relying on the opinions of Lipman who sees that the compound thinking consists of creative thinking and critical thinking, In accordance with this point of view, researchers have resorted to build scale of the critical thinking in its final form of (28) item additionally to the adoption of Torrance for the creative thinking which was translated by sayed. Khairallah in 1981 after confirming psychometric Properties Of both scales and then collect scores of both scales and be the final score represented the level of the compound thinking that has been shown by the results of secondary school students they have no skill of
... Show MoreThe appearance of Mixed Mode Oscillations (MMOs) and chaotic spiking in a Light Emitting Diode (LED) with optoelectronic feedback theoretically and experimentally have been reported. The transition between periodic and chaotic mixed-mode states has been investigated by varying feedback strength. In incoherent semiconductor chaotically spiking attractors with optoelectronic feedback have been observed to be the result of canard phenomena in three-dimensional phase space (incomplete homoclinic scenarios).
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With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr
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In this paper, we introduce the concept of Jordan –algebra, special Jordan –algebra and triple –homomorphisms. We also introduce Bi - –derivations and Annihilator of Jordan algebra. Finally, we study the triple –homomorphisms and Bi - –derivations on Jordan algebra.
In this article an attempt has been made to procure the concept of pairwise neutrosophic simply open set, pairwise neutrosophic simply continuous mapping, pairwise neutrosophic simply open mapping, pairwise neutrosophic simply compactness, pairwise neutrosophic simply b-open set, pairwise neutrosophic simply b-continuous mapping, pairwise neutrosophic simply b-open mapping and pairwise neutrosophic simply b-compactness via neutrosophic bi-topological spaces (in short NBTS). Besides, we furnish few illustrative examples on them via NBTS. Further, we investigate some basic properties of them, and formulate several results on NBTSs.
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