Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem. In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a local improvement operator to effectively discover community structure in the modular complex networks when employing the modularity density metric as a single-objective function. The framework of the proposed algorithm consists of three main steps: an initialization strategy, a movement strategy based on perturbation genetic operators, and an improvement operator. The key idea behind the improvement operator is to determine and reassign the complex network nodes that are located in the wrong communities if the majority of their topological links do not belong to their current communities, making it appear that these nodes belong to another community. The performance of the proposed algorithm has been tested and evaluated when applied to publicly-available modular complex networks generated using a flexible and simple benchmark generator. The experimental results showed the effectiveness of the suggested method in discovering community structure over modular networks of different complexities and sizes.
In this paper, the oscillatory properties and asymptotic behaviour of a third-order three-dimensional neutral system are discussed. Some sufficient conditions are obtained to ensure that all bounded positive solutions of the system are oscillatory or non-oscillatory. On the other hand, the non-oscillatory solutions either converge or diverge when goes to infinity. A special technique is adopted to include all possible cases. The obtained results include illustrative examples.
In this paper, we studied the travelling wave solving for some models of Burger's equations. We used sine-cosine method to solution nonlinear equation and we used direct solution after getting travelling wave equation.
In this research, geopolymer mortar had to be designed with 50% to 50% slag and fly ash with and without 1% micro steel fiber at curing temperature of 240℃. The molarity of alkaline solution adjusted with 12 molar sodium hydroxid to sodium silicate was 2 to 1, reaspectivly. The heat of curing increased the geopolymerization proceses of geoplymer mortar, which led to increasing strength, giving the best result and early curing age. The heat was applied for two days by four hours each day. It was discovered in the impact test that the value first crack of each mix was somewhat similar, but the failure increased 72% for the mixture that did not contain fiber. For the energy observation results it was shown that the mixt
... Show MoreThe aim of this paper is to investigate the theoretical approach for solvability of impulsive abstract Cauchy problem for impulsive nonlinear fractional order partial differential equations with nonlocal conditions, where the nonlinear extensible beam equation is a particular application case of this problem.
Sixty samples of commercially available contact lens solutions were collected from students at the Pharmacy College/Baghdad University. The types of lenses used varied from medical to cosmetic. They were cultured to diagnose any microbial contamination within the solutions. Both used and unused solutions were subject for culturing. Thirty six (60%) used samples showed bacterial growth, fungal growth was absent. Pseudomonas aeruginosa accounts for the highest number of isolates (25%) followed by E. coli (21%), Staphylococcus epidermidis (6.6%), Pseudomonas fluorescence (5%) and Proteus mirabilis (1.6%) respectively. Only one (1) unused (sealed) sample showed growth of P. fluorescence.
... Show MoreThe objective of this work is to study the influence of α-amylase enzymatic solution immersion, soil burial and water immersion on the biodegradability behavior of polyvinyl alcohol (PVA) /Corn Starch (CS) blend films Polyvinyl alcohol (PVA) /Corn Starch (CS) blend films were prepared by solution casting method with different weight percentages of PVA(0%,10%,30%,50%,70% and 90%) . The biodegradability of the films has been investigated by determination the weight loss of the tested films. It was noticed that the films containing corn starch were highly biodegraded under above influences. The weight loss of the tested films decreased with increasing PVA content and increased with immersion time in enzymatic solution and water and soil bu
... Show MoreIn this research paper, we explain the use of the convexity and the starlikness properties of a given function to generate special properties of differential subordination and superordination functions in the classes of analytic functions that have the form in the unit disk. We also show the significant of these properties to derive sandwich results when the Srivastava- Attiya operator is used.
The problem of text recognition and its applicability as part of images captured in the wild has gained a significant attention from the computer vision community in recent years. In contrast to the recognition of printed documents, scene text recognition is a difficult problem. Contrary to recognition of printed documents, recognizing a scene text is a challenging problem. Many researches focus on the problem of recognizing text extracted from natural scene images. Significant attempts have been made to address this problem in recent past. However, many of these attempts work on utilizing availability of strong context, which naturally limits the dictionary. This paper presents a review of recent papers related to scene text
... Show MoreBreast cancer is the second deadliest disease infected women worldwide. For this
reason the early detection is one of the most essential stop to overcomeit dependingon
automatic devices like artificial intelligent. Medical applications of machine learning
algorithmsare mostly based on their ability to handle classification problems,
including classifications of illnesses or to estimate prognosis. Before machine
learningis applied for diagnosis, it must be trained first. The research methodology
which isdetermines differentofmachine learning algorithms,such as Random tree,
ID3, CART, SMO, C4.5 and Naive Bayesto finds the best training algorithm result.
The contribution of this research is test the data set with mis
Image classification can be defined as one of the most important tasks in the area of machine learning. Recently, deep neural networks, especially deep convolution networks, have participated greatly in end-to-end learning which reduce need for human designed features in the image recognition like Convolution Neural Network. It is offers the computation models which are made up of several processing layers for learning data representations with several abstraction levels. In this work, a pre-trained deep CNN is utilized according to some parameters like filter size, no of convolution, pooling, fully connected and type of activation function which includes 300 images for training and predict 100 image gender using probability measures. Re
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